Top 10 important data science concepts
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
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1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
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This post is for beginners who decided to learn Data Science. I want to tell you that becoming a data scientist is a journey (6 months - 1 year at least) and not a 1 month thing where u do some courses and you are a data scientist. There are different fields in Data Science that you have to first get familiar and strong in basics as well as do hands-on to get the abilities that are required to function in a full time job opportunity. Then further delve into advanced implementations.
There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below:
Basic Statistics, Linear Algebra, calculus, probability
Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science.
Machine Learning - All of the above will be used here to implement machine learning concepts.
Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc.
This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order.
You can use the below Sources to prepare your own roadmap:
@free4unow_backup - some free courses from here
@datasciencefun - data science and machines learning resources
Data Science - https://365datascience.pxf.io/q4m66g
Python - https://bit.ly/45rlWZE
Kaggle - https://www.kaggle.com/learn
There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below:
Basic Statistics, Linear Algebra, calculus, probability
Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science.
Machine Learning - All of the above will be used here to implement machine learning concepts.
Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc.
This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order.
You can use the below Sources to prepare your own roadmap:
@free4unow_backup - some free courses from here
@datasciencefun - data science and machines learning resources
Data Science - https://365datascience.pxf.io/q4m66g
Python - https://bit.ly/45rlWZE
Kaggle - https://www.kaggle.com/learn
๐4๐1๐คฃ1
10 Must-Know Machine Learning Algorithms for Beginners
1๏ธโฃ Linear Regression:
Predicts a continuous outcome by fitting a linear relationship between independent and dependent variables.
2๏ธโฃ Logistic Regression:
Estimates binary outcomes by predicting the probability of an event using a logit function.
3๏ธโฃ Decision Tree:
Splits data into homogeneous sets based on significant attributes to classify or predict outcomes.
4๏ธโฃ SVM (Support Vector Machine):
Classifies data by finding the optimal hyperplane that separates data points in an n-dimensional space.
5๏ธโฃ Naive Bayes:
Predicts outcomes by assuming independence between features and calculating probabilities based on Bayes' theorem.
6๏ธโฃ KNN (K-Nearest Neighbors):
Classifies data points based on the majority vote of their nearest neighbors in the feature space.
7๏ธโฃ K-Means:
Groups data into K clusters by minimizing the distance between data points and cluster centroids.
8๏ธโฃ Random Forest:
Combines multiple decision trees to improve prediction accuracy through majority voting.
9๏ธโฃ Dimensionality Reduction Algorithms:
Reduces the number of features in data while preserving important patterns and relationships.
๐ Gradient Boosting & AdaBoosting: Combines weak predictive models to create a strong model, improving accuracy and robustness.
1๏ธโฃ Linear Regression:
Predicts a continuous outcome by fitting a linear relationship between independent and dependent variables.
2๏ธโฃ Logistic Regression:
Estimates binary outcomes by predicting the probability of an event using a logit function.
3๏ธโฃ Decision Tree:
Splits data into homogeneous sets based on significant attributes to classify or predict outcomes.
4๏ธโฃ SVM (Support Vector Machine):
Classifies data by finding the optimal hyperplane that separates data points in an n-dimensional space.
5๏ธโฃ Naive Bayes:
Predicts outcomes by assuming independence between features and calculating probabilities based on Bayes' theorem.
6๏ธโฃ KNN (K-Nearest Neighbors):
Classifies data points based on the majority vote of their nearest neighbors in the feature space.
7๏ธโฃ K-Means:
Groups data into K clusters by minimizing the distance between data points and cluster centroids.
8๏ธโฃ Random Forest:
Combines multiple decision trees to improve prediction accuracy through majority voting.
9๏ธโฃ Dimensionality Reduction Algorithms:
Reduces the number of features in data while preserving important patterns and relationships.
๐ Gradient Boosting & AdaBoosting: Combines weak predictive models to create a strong model, improving accuracy and robustness.
๐1๐1
๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐๐. ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ ๐๐. ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐. ๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ
๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐
Think of them as data detectives.
โ ๐ ๐จ๐๐ฎ๐ฌ: Identifying patterns and building predictive models.
โ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: Machine learning, statistics, Python/R.
โ ๐๐จ๐จ๐ฅ๐ฌ: Jupyter Notebooks, TensorFlow, PyTorch.
โ ๐๐จ๐๐ฅ: Extract actionable insights from raw data.
๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐: Creating a recommendation system like Netflix.
๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ
The architects of data infrastructure.
โ ๐ ๐จ๐๐ฎ๐ฌ: Developing data pipelines, storage systems, and infrastructure. โ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: SQL, Big Data technologies (Hadoop, Spark), cloud platforms.
โ ๐๐จ๐จ๐ฅ๐ฌ: Airflow, Kafka, Snowflake.
โ ๐๐จ๐๐ฅ: Ensure seamless data flow across the organization.
๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐: Designing a pipeline to handle millions of transactions in real-time.
๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐
Data storytellers.
โ ๐ ๐จ๐๐ฎ๐ฌ: Creating visualizations, dashboards, and reports.
โ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: Excel, Tableau, SQL.
โ ๐๐จ๐จ๐ฅ๐ฌ: Power BI, Looker, Google Sheets.
โ ๐๐จ๐๐ฅ: Help businesses make data-driven decisions.
๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐: Analyzing campaign data to optimize marketing strategies.
๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ
The connectors between data science and software engineering.
โ ๐ ๐จ๐๐ฎ๐ฌ: Deploying machine learning models into production.
โ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: Python, APIs, cloud services (AWS, Azure).
โ ๐๐จ๐จ๐ฅ๐ฌ: Kubernetes, Docker, FastAPI.
โ ๐๐จ๐๐ฅ: Make models scalable and ready for real-world applications. ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐: Deploying a fraud detection model for a bank.
๐ช๐ต๐ฎ๐ ๐ฃ๐ฎ๐๐ต ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐ฌ๐ผ๐ ๐๐ต๐ผ๐ผ๐๐ฒ?
โ Love solving complex problems?
โ Data Scientist
โ Enjoy working with systems and Big Data?
โ Data Engineer
โ Passionate about visual storytelling?
โ Data Analyst
โ Excited to scale AI systems?
โ ML Engineer
Each role is crucial and in demandโchoose based on your strengths and career aspirations.
Whatโs your ideal role?
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content
ENJOY LEARNING ๐๐
๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐
Think of them as data detectives.
โ ๐ ๐จ๐๐ฎ๐ฌ: Identifying patterns and building predictive models.
โ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: Machine learning, statistics, Python/R.
โ ๐๐จ๐จ๐ฅ๐ฌ: Jupyter Notebooks, TensorFlow, PyTorch.
โ ๐๐จ๐๐ฅ: Extract actionable insights from raw data.
๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐: Creating a recommendation system like Netflix.
๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ
The architects of data infrastructure.
โ ๐ ๐จ๐๐ฎ๐ฌ: Developing data pipelines, storage systems, and infrastructure. โ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: SQL, Big Data technologies (Hadoop, Spark), cloud platforms.
โ ๐๐จ๐จ๐ฅ๐ฌ: Airflow, Kafka, Snowflake.
โ ๐๐จ๐๐ฅ: Ensure seamless data flow across the organization.
๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐: Designing a pipeline to handle millions of transactions in real-time.
๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐
Data storytellers.
โ ๐ ๐จ๐๐ฎ๐ฌ: Creating visualizations, dashboards, and reports.
โ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: Excel, Tableau, SQL.
โ ๐๐จ๐จ๐ฅ๐ฌ: Power BI, Looker, Google Sheets.
โ ๐๐จ๐๐ฅ: Help businesses make data-driven decisions.
๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐: Analyzing campaign data to optimize marketing strategies.
๐ ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ
The connectors between data science and software engineering.
โ ๐ ๐จ๐๐ฎ๐ฌ: Deploying machine learning models into production.
โ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ: Python, APIs, cloud services (AWS, Azure).
โ ๐๐จ๐จ๐ฅ๐ฌ: Kubernetes, Docker, FastAPI.
โ ๐๐จ๐๐ฅ: Make models scalable and ready for real-world applications. ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐: Deploying a fraud detection model for a bank.
๐ช๐ต๐ฎ๐ ๐ฃ๐ฎ๐๐ต ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐ฌ๐ผ๐ ๐๐ต๐ผ๐ผ๐๐ฒ?
โ Love solving complex problems?
โ Data Scientist
โ Enjoy working with systems and Big Data?
โ Data Engineer
โ Passionate about visual storytelling?
โ Data Analyst
โ Excited to scale AI systems?
โ ML Engineer
Each role is crucial and in demandโchoose based on your strengths and career aspirations.
Whatโs your ideal role?
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content
ENJOY LEARNING ๐๐
โค6๐4๐คฃ1
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Key Concepts for Machine Learning Interviews
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayesโ theorem, prior and posterior distributions, and Bayesian networks.
Python Programming Resources
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https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Like if you need similar content ๐๐
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayesโ theorem, prior and posterior distributions, and Bayesian networks.
Python Programming Resources
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https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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Here are some best Telegram Channels for free education in 2024
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Web Development Free Resources
Data Science & Machine Learning
Programming Free Books
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Crack your coding Interviews
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If you want to get a job as a machine learning engineer, donโt start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
โค3๐3๐1
Here are some project ideas for a data science and machine learning project focused on generating AI:
1. Natural Language Generation (NLG) Model: Build a model that generates human-like text based on input data. This could be used for creating product descriptions, news articles, or personalized recommendations.
2. Code Generation Model: Develop a model that generates code snippets based on a given task or problem statement. This could help automate software development tasks or assist programmers in writing code more efficiently.
3. Image Captioning Model: Create a model that generates captions for images, describing the content of the image in natural language. This could be useful for visually impaired individuals or for enhancing image search capabilities.
4. Music Generation Model: Build a model that generates music compositions based on input data, such as existing songs or musical patterns. This could be used for creating background music for videos or games.
5. Video Synthesis Model: Develop a model that generates realistic video sequences based on input data, such as a series of images or a textual description. This could be used for generating synthetic training data for computer vision models.
6. Chatbot Generation Model: Create a model that generates conversational agents or chatbots based on input data, such as dialogue datasets or user interactions. This could be used for customer service automation or virtual assistants.
7. Art Generation Model: Build a model that generates artistic images or paintings based on input data, such as art styles, color palettes, or themes. This could be used for creating unique digital artwork or personalized designs.
8. Story Generation Model: Develop a model that generates fictional stories or narratives based on input data, such as plot outlines, character descriptions, or genre preferences. This could be used for creative writing prompts or interactive storytelling applications.
9. Recipe Generation Model: Create a model that generates new recipes based on input data, such as ingredient lists, dietary restrictions, or cuisine preferences. This could be used for meal planning or culinary inspiration.
10. Financial Report Generation Model: Build a model that generates financial reports or summaries based on input data, such as company financial statements, market trends, or investment portfolios. This could be used for automated financial analysis or decision-making support.
Any project which sounds interesting to you?
1. Natural Language Generation (NLG) Model: Build a model that generates human-like text based on input data. This could be used for creating product descriptions, news articles, or personalized recommendations.
2. Code Generation Model: Develop a model that generates code snippets based on a given task or problem statement. This could help automate software development tasks or assist programmers in writing code more efficiently.
3. Image Captioning Model: Create a model that generates captions for images, describing the content of the image in natural language. This could be useful for visually impaired individuals or for enhancing image search capabilities.
4. Music Generation Model: Build a model that generates music compositions based on input data, such as existing songs or musical patterns. This could be used for creating background music for videos or games.
5. Video Synthesis Model: Develop a model that generates realistic video sequences based on input data, such as a series of images or a textual description. This could be used for generating synthetic training data for computer vision models.
6. Chatbot Generation Model: Create a model that generates conversational agents or chatbots based on input data, such as dialogue datasets or user interactions. This could be used for customer service automation or virtual assistants.
7. Art Generation Model: Build a model that generates artistic images or paintings based on input data, such as art styles, color palettes, or themes. This could be used for creating unique digital artwork or personalized designs.
8. Story Generation Model: Develop a model that generates fictional stories or narratives based on input data, such as plot outlines, character descriptions, or genre preferences. This could be used for creative writing prompts or interactive storytelling applications.
9. Recipe Generation Model: Create a model that generates new recipes based on input data, such as ingredient lists, dietary restrictions, or cuisine preferences. This could be used for meal planning or culinary inspiration.
10. Financial Report Generation Model: Build a model that generates financial reports or summaries based on input data, such as company financial statements, market trends, or investment portfolios. This could be used for automated financial analysis or decision-making support.
Any project which sounds interesting to you?
โค1๐1
For those of you who are new to Data Science and Machine learning algorithms, let me try to give you a brief overview. ML Algorithms can be categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content
ENJOY LEARNING ๐๐
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content
ENJOY LEARNING ๐๐
๐4๐1
Data Science Tip๐ก
Always start with ๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐๐ฒ ๐ฆ๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ before jumping into complex models.
โข Understand Descriptive vs. Inferential Statistics: Descriptive summarizes; Inferential predicts.
โข Use the Empirical Rule (68-95-99.7) to grasp normal distribution probabilities.
โข Apply standard deviation and variance to quantify data spread.
โข Leverage probability distributions like PMF, PDF, and CDF for modeling.
โข Explore correlation vs. covariance to uncover variable relationships.
Are your insights actionable enough?
Statistics is often misused, leading to flawed conclusions. But is your interpretation meaningful enough to drive decisions?
โณ Focus on ๐ฐ๐น๐ฎ๐ฟ๐ถ๐๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐ป๐๐ฒ๐ ๐:
โข Identify whether data follows a normal distribution using Q-Q plots.
โข Use visualizations like boxplots and histograms for a quick overview.
โข Incorporate parametric and non-parametric methods for density estimations.
โข Avoid misrepresentation by understanding skewness and kurtosis.
โข Validate results with statistical tests like Shapiro-Wilk for normality.
See how much you improve ๐๐ผ๐๐ฟ ๐ฑ๐ฒ๐ฐ๐ถ๐๐ถ๐ผ๐ป๐.
Always start with ๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐๐ฒ ๐ฆ๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ before jumping into complex models.
โข Understand Descriptive vs. Inferential Statistics: Descriptive summarizes; Inferential predicts.
โข Use the Empirical Rule (68-95-99.7) to grasp normal distribution probabilities.
โข Apply standard deviation and variance to quantify data spread.
โข Leverage probability distributions like PMF, PDF, and CDF for modeling.
โข Explore correlation vs. covariance to uncover variable relationships.
Are your insights actionable enough?
Statistics is often misused, leading to flawed conclusions. But is your interpretation meaningful enough to drive decisions?
โณ Focus on ๐ฐ๐น๐ฎ๐ฟ๐ถ๐๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐ป๐๐ฒ๐ ๐:
โข Identify whether data follows a normal distribution using Q-Q plots.
โข Use visualizations like boxplots and histograms for a quick overview.
โข Incorporate parametric and non-parametric methods for density estimations.
โข Avoid misrepresentation by understanding skewness and kurtosis.
โข Validate results with statistical tests like Shapiro-Wilk for normality.
See how much you improve ๐๐ผ๐๐ฟ ๐ฑ๐ฒ๐ฐ๐ถ๐๐ถ๐ผ๐ป๐.
๐1
Statistics Roadmap for Data Science!
Phase 1: Fundamentals of Statistics
1๏ธโฃ Basic Concepts
-Introduction to Statistics
-Types of Data
-Descriptive Statistics
2๏ธโฃ Probability
-Basic Probability
-Conditional Probability
-Probability Distributions
Phase 2: Intermediate Statistics
3๏ธโฃ Inferential Statistics
-Sampling and Sampling Distributions
-Hypothesis Testing
-Confidence Intervals
4๏ธโฃ Regression Analysis
-Linear Regression
-Diagnostics and Validation
Phase 3: Advanced Topics
5๏ธโฃ Advanced Probability and Statistics
-Advanced Probability Distributions
-Bayesian Statistics
6๏ธโฃ Multivariate Statistics
-Principal Component Analysis (PCA)
-Clustering
Phase 4: Statistical Learning and Machine Learning
7๏ธโฃ Statistical Learning
-Introduction to Statistical Learning
-Supervised Learning
-Unsupervised Learning
Phase 5: Practical Application
8๏ธโฃ Tools and Software
-Statistical Software (R, Python)
-Data Visualization (Matplotlib, Seaborn, ggplot2)
9๏ธโฃ Projects and Case Studies
-Capstone Project
-Case Studies
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
Phase 1: Fundamentals of Statistics
1๏ธโฃ Basic Concepts
-Introduction to Statistics
-Types of Data
-Descriptive Statistics
2๏ธโฃ Probability
-Basic Probability
-Conditional Probability
-Probability Distributions
Phase 2: Intermediate Statistics
3๏ธโฃ Inferential Statistics
-Sampling and Sampling Distributions
-Hypothesis Testing
-Confidence Intervals
4๏ธโฃ Regression Analysis
-Linear Regression
-Diagnostics and Validation
Phase 3: Advanced Topics
5๏ธโฃ Advanced Probability and Statistics
-Advanced Probability Distributions
-Bayesian Statistics
6๏ธโฃ Multivariate Statistics
-Principal Component Analysis (PCA)
-Clustering
Phase 4: Statistical Learning and Machine Learning
7๏ธโฃ Statistical Learning
-Introduction to Statistical Learning
-Supervised Learning
-Unsupervised Learning
Phase 5: Practical Application
8๏ธโฃ Tools and Software
-Statistical Software (R, Python)
-Data Visualization (Matplotlib, Seaborn, ggplot2)
9๏ธโฃ Projects and Case Studies
-Capstone Project
-Case Studies
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐4โค1๐1
Key Concepts for Machine Learning Interviews
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayesโ theorem, prior and posterior distributions, and Bayesian networks.
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayesโ theorem, prior and posterior distributions, and Bayesian networks.
๐5๐คฃ1
๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป:
How does outliers impact kNN?
Outliers can significantly impact the performance of kNN, leading to inaccurate predictions due to the model's reliance on proximity for decision-making. Hereโs a breakdown of how outliers influence kNN:
๐๐ถ๐ด๐ต ๐ฉ๐ฎ๐ฟ๐ถ๐ฎ๐ป๐ฐ๐ฒ
The presence of outliers can increase the model's variance, as predictions near outliers may fluctuate unpredictably depending on which neighbors are included. This makes the model less reliable for regression tasks with scattered or sparse data.
๐๐ถ๐๐๐ฎ๐ป๐ฐ๐ฒ ๐ ๐ฒ๐๐ฟ๐ถ๐ฐ ๐ฆ๐ฒ๐ป๐๐ถ๐๐ถ๐๐ถ๐๐
kNN relies on distance metrics, which can be significantly affected by outliers. In high-dimensional spaces, outliers can increase the range of distances, making it harder for the algorithm to distinguish between nearby points and those farther away. This issue can lead to an overall reduction in accuracy as the modelโs ability to effectively measure "closeness" degrades.
๐ฅ๐ฒ๐ฑ๐๐ฐ๐ฒ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐ถ๐ป ๐๐น๐ฎ๐๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป/๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป ๐ง๐ฎ๐๐ธ๐
Outliers near class boundaries can pull the decision boundary toward them, potentially misclassifying nearby points that should belong to a different class. This is particularly problematic if k is small, as individual points (like outliers) have a greater influence. The same happens in regression tasks as well.
๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ณ๐น๐๐ฒ๐ป๐ฐ๐ฒ ๐๐ถ๐๐ฝ๐ฟ๐ผ๐ฝ๐ผ๐ฟ๐๐ถ๐ผ๐ป
If certain features contain outliers, they can dominate the distance calculations and overshadow the impact of other features. For example, an outlier in a high-magnitude feature may cause distances to be determined largely by that feature, affecting the quality of the neighbor selection.
ENJOY LEARNING ๐๐
How does outliers impact kNN?
Outliers can significantly impact the performance of kNN, leading to inaccurate predictions due to the model's reliance on proximity for decision-making. Hereโs a breakdown of how outliers influence kNN:
๐๐ถ๐ด๐ต ๐ฉ๐ฎ๐ฟ๐ถ๐ฎ๐ป๐ฐ๐ฒ
The presence of outliers can increase the model's variance, as predictions near outliers may fluctuate unpredictably depending on which neighbors are included. This makes the model less reliable for regression tasks with scattered or sparse data.
๐๐ถ๐๐๐ฎ๐ป๐ฐ๐ฒ ๐ ๐ฒ๐๐ฟ๐ถ๐ฐ ๐ฆ๐ฒ๐ป๐๐ถ๐๐ถ๐๐ถ๐๐
kNN relies on distance metrics, which can be significantly affected by outliers. In high-dimensional spaces, outliers can increase the range of distances, making it harder for the algorithm to distinguish between nearby points and those farther away. This issue can lead to an overall reduction in accuracy as the modelโs ability to effectively measure "closeness" degrades.
๐ฅ๐ฒ๐ฑ๐๐ฐ๐ฒ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐ถ๐ป ๐๐น๐ฎ๐๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป/๐ฅ๐ฒ๐ด๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป ๐ง๐ฎ๐๐ธ๐
Outliers near class boundaries can pull the decision boundary toward them, potentially misclassifying nearby points that should belong to a different class. This is particularly problematic if k is small, as individual points (like outliers) have a greater influence. The same happens in regression tasks as well.
๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ณ๐น๐๐ฒ๐ป๐ฐ๐ฒ ๐๐ถ๐๐ฝ๐ฟ๐ผ๐ฝ๐ผ๐ฟ๐๐ถ๐ผ๐ป
If certain features contain outliers, they can dominate the distance calculations and overshadow the impact of other features. For example, an outlier in a high-magnitude feature may cause distances to be determined largely by that feature, affecting the quality of the neighbor selection.
ENJOY LEARNING ๐๐
๐4๐1๐คฃ1
Complete Roadmap to learn Machine Learning and Artificial Intelligence
๐๐
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
๐๐
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
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Complete Roadmap to become a data scientist in 5 months
Free Resources to learn Data Science: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Week 1-2: Fundamentals
- Day 1-3: Introduction to Data Science, its applications, and roles.
- Day 4-7: Brush up on Python programming.
- Day 8-10: Learn basic statistics and probability.
Week 3-4: Data Manipulation and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.
Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.
Work on Data Science Projects: https://t.iss.one/pythonspecialist/29
Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.
Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.
Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).
Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).
Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).
Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.
Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.
Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.
Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.
Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.
Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.
Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.
Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.
Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.
ENJOY LEARNING ๐๐
Free Resources to learn Data Science: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Week 1-2: Fundamentals
- Day 1-3: Introduction to Data Science, its applications, and roles.
- Day 4-7: Brush up on Python programming.
- Day 8-10: Learn basic statistics and probability.
Week 3-4: Data Manipulation and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.
Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.
Work on Data Science Projects: https://t.iss.one/pythonspecialist/29
Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.
Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.
Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).
Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).
Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).
Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.
Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.
Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.
Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.
Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.
Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.
Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.
Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.
Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.
ENJOY LEARNING ๐๐
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Many data scientists don't know how to push ML models to production. Here's the recipe ๐
๐๐ฒ๐ ๐๐ป๐ด๐ฟ๐ฒ๐ฑ๐ถ๐ฒ๐ป๐๐
๐น ๐ง๐ฟ๐ฎ๐ถ๐ป / ๐ง๐ฒ๐๐ ๐๐ฎ๐๐ฎ๐๐ฒ๐ - Ensure Test is representative of Online data
๐น ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ - Generate features in real-time
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ข๐ฏ๐ท๐ฒ๐ฐ๐ - Trained SkLearn or Tensorflow Model
๐น ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ผ๐ฑ๐ฒ ๐ฅ๐ฒ๐ฝ๐ผ - Save model project code to Github
๐น ๐๐ฃ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ - Use FastAPI or Flask to build a model API
๐น ๐๐ผ๐ฐ๐ธ๐ฒ๐ฟ - Containerize the ML model API
๐น ๐ฅ๐ฒ๐บ๐ผ๐๐ฒ ๐ฆ๐ฒ๐ฟ๐๐ฒ๐ฟ - Choose a cloud service; e.g. AWS sagemaker
๐น ๐จ๐ป๐ถ๐ ๐ง๐ฒ๐๐๐ - Test inputs & outputs of functions and APIs
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด - Evidently AI, a simple, open-source for ML monitoring
๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐ฑ๐๐ฟ๐ฒ
๐ฆ๐๐ฒ๐ฝ ๐ญ - ๐๐ฎ๐๐ฎ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป & ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด
Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling.
๐ฆ๐๐ฒ๐ฝ ๐ฎ - ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐
Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.
๐ฆ๐๐ฒ๐ฝ ๐ฏ - ๐๐ฃ๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐ & ๐๐ผ๐ป๐๐ฎ๐ถ๐ป๐ฒ๐ฟ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป
Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment
๐ฆ๐๐ฒ๐ฝ ๐ฐ - ๐ง๐ฒ๐๐๐ถ๐ป๐ด & ๐๐ฒ๐ฝ๐น๐ผ๐๐บ๐ฒ๐ป๐
Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.
๐ฆ๐๐ฒ๐ฝ ๐ฑ - ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด
Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.
๐๐ฒ๐ ๐๐ป๐ด๐ฟ๐ฒ๐ฑ๐ถ๐ฒ๐ป๐๐
๐น ๐ง๐ฟ๐ฎ๐ถ๐ป / ๐ง๐ฒ๐๐ ๐๐ฎ๐๐ฎ๐๐ฒ๐ - Ensure Test is representative of Online data
๐น ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ - Generate features in real-time
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ข๐ฏ๐ท๐ฒ๐ฐ๐ - Trained SkLearn or Tensorflow Model
๐น ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ผ๐ฑ๐ฒ ๐ฅ๐ฒ๐ฝ๐ผ - Save model project code to Github
๐น ๐๐ฃ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ - Use FastAPI or Flask to build a model API
๐น ๐๐ผ๐ฐ๐ธ๐ฒ๐ฟ - Containerize the ML model API
๐น ๐ฅ๐ฒ๐บ๐ผ๐๐ฒ ๐ฆ๐ฒ๐ฟ๐๐ฒ๐ฟ - Choose a cloud service; e.g. AWS sagemaker
๐น ๐จ๐ป๐ถ๐ ๐ง๐ฒ๐๐๐ - Test inputs & outputs of functions and APIs
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด - Evidently AI, a simple, open-source for ML monitoring
๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐ฑ๐๐ฟ๐ฒ
๐ฆ๐๐ฒ๐ฝ ๐ญ - ๐๐ฎ๐๐ฎ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป & ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด
Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling.
๐ฆ๐๐ฒ๐ฝ ๐ฎ - ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐
Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.
๐ฆ๐๐ฒ๐ฝ ๐ฏ - ๐๐ฃ๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐ & ๐๐ผ๐ป๐๐ฎ๐ถ๐ป๐ฒ๐ฟ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป
Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment
๐ฆ๐๐ฒ๐ฝ ๐ฐ - ๐ง๐ฒ๐๐๐ถ๐ป๐ด & ๐๐ฒ๐ฝ๐น๐ผ๐๐บ๐ฒ๐ป๐
Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.
๐ฆ๐๐ฒ๐ฝ ๐ฑ - ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด
Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.
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Essential Python Libraries for Data Analytics ๐๐
Python Free Resources: https://t.iss.one/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python Free Resources: https://t.iss.one/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
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