7 Websites to Learn Data Science for FREE๐งโ๐ป
โ w3school
โ datasimplifier
โ hackerrank
โ kaggle
โ geeksforgeeks
โ leetcode
โ freecodecamp
โ w3school
โ datasimplifier
โ hackerrank
โ kaggle
โ geeksforgeeks
โ leetcode
โ freecodecamp
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Use of Machine Learning in Data Analytics
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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
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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
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Basics of Machine Learning ๐๐
Free Resources to learn Machine Learning: https://t.iss.one/free4unow_backup/587
Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:
1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
Join @datasciencefun for more
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Free Resources to learn Machine Learning: https://t.iss.one/free4unow_backup/587
Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:
1. Supervised Learning: The algorithm is trained on a labeled dataset, learning to map input to output. For example, it can predict housing prices based on features like size and location.
2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.
3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.
Key concepts include:
- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.
- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.
- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.
- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.
In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.
Join @datasciencefun for more
ENJOY LEARNING ๐๐
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Which python library is not used specifically for data visualization?
Anonymous Quiz
12%
Matplotlib
14%
Seaborn
58%
Numpy
16%
Plotly
๐2
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
๐8โค6
How to get started with data science
Many people who get interested in learning data science don't really know what it's all about.
They start coding just for the sake of it and on first challenge or problem they can't solve, they quit.
Just like other disciplines in tech, data science is challenging and requires a level of critical thinking and problem solving attitude.
If you're among people who want to get started with data science but don't know how - I have something amazing for you!
I created Best Data Science & Machine Learning Resources that will help you organize your career in data.
Happy learning ๐๐
Many people who get interested in learning data science don't really know what it's all about.
They start coding just for the sake of it and on first challenge or problem they can't solve, they quit.
Just like other disciplines in tech, data science is challenging and requires a level of critical thinking and problem solving attitude.
If you're among people who want to get started with data science but don't know how - I have something amazing for you!
I created Best Data Science & Machine Learning Resources that will help you organize your career in data.
Happy learning ๐๐
๐4โค1๐ข1
Data Science is very vast field.
I saw one linkedin profile today with below skills ๐
Technical Skills:
Data Manipulation: Numpy, Pandas, BeautifulSoup, PySpark
Data Visualization: EDA- Matplotlib, Seaborn, Plotly, Tableau, PowerBI
Machine Learning: Scikit-Learn, TimeSeries Analysis
MLOPs: Gensinms, Github Actions, Gitlab CI/CD, mlflows, WandB, comet
Deep Learning: PyTorch, TensorFlow, Keras
Natural Language Processing: NLTK, NER, Spacy, word2vec, Kmeans, KNN, DBscan
Computer Vision: openCV, Yolo-V5, unet, cnn, resnet
Version Control: Git, Github, Gitlab
Database: SQL, NOSQL, Databricks
Web Frameworks: Streamlit, Flask, FastAPI, Streamlit
Generative AI - HuggingFace, LLM, Langchain, GPT-3.5, and GPT-4
Project Management and collaboration tool- JIRA, Confluence
Deployment- AWS, GCP, Docker, Google Vertex AI, Data Robot AI, Big ML, Microsoft Azure
How many of them do you have?
I saw one linkedin profile today with below skills ๐
Technical Skills:
Data Manipulation: Numpy, Pandas, BeautifulSoup, PySpark
Data Visualization: EDA- Matplotlib, Seaborn, Plotly, Tableau, PowerBI
Machine Learning: Scikit-Learn, TimeSeries Analysis
MLOPs: Gensinms, Github Actions, Gitlab CI/CD, mlflows, WandB, comet
Deep Learning: PyTorch, TensorFlow, Keras
Natural Language Processing: NLTK, NER, Spacy, word2vec, Kmeans, KNN, DBscan
Computer Vision: openCV, Yolo-V5, unet, cnn, resnet
Version Control: Git, Github, Gitlab
Database: SQL, NOSQL, Databricks
Web Frameworks: Streamlit, Flask, FastAPI, Streamlit
Generative AI - HuggingFace, LLM, Langchain, GPT-3.5, and GPT-4
Project Management and collaboration tool- JIRA, Confluence
Deployment- AWS, GCP, Docker, Google Vertex AI, Data Robot AI, Big ML, Microsoft Azure
How many of them do you have?
๐4
A-Z of essential data science concepts
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count 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
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count 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
๐7โค4๐1๐คฉ1
๐ Free useful resources to learn Machine Learning
๐ Google
https://developers.google.com/machine-learning/crash-course
๐ Leetcode
https://leetcode.com/explore/featured/card/machine-learning-101
๐ Hackerrank
https://www.hackerrank.com/domains/ai/machine-learning
๐ Hands-on Machine Learning
https://t.iss.one/datasciencefun/424
๐ FreeCodeCamp
https://www.freecodecamp.org/learn/machine-learning-with-python/
๐ Machine learning projects
https://t.iss.one/datasciencefun/392
๐ Kaggle
https://www.kaggle.com/learn/intro-to-machine-learning
https://www.kaggle.com/learn/intermediate-machine-learning
๐ Geeksforgeeks
https://www.geeksforgeeks.org/machine-learning/
๐ Create ML Models
https://docs.microsoft.com/en-us/learn/paths/create-machine-learn-models/
๐ Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
๐ Google
https://developers.google.com/machine-learning/crash-course
๐ Leetcode
https://leetcode.com/explore/featured/card/machine-learning-101
๐ Hackerrank
https://www.hackerrank.com/domains/ai/machine-learning
๐ Hands-on Machine Learning
https://t.iss.one/datasciencefun/424
๐ FreeCodeCamp
https://www.freecodecamp.org/learn/machine-learning-with-python/
๐ Machine learning projects
https://t.iss.one/datasciencefun/392
๐ Kaggle
https://www.kaggle.com/learn/intro-to-machine-learning
https://www.kaggle.com/learn/intermediate-machine-learning
๐ Geeksforgeeks
https://www.geeksforgeeks.org/machine-learning/
๐ Create ML Models
https://docs.microsoft.com/en-us/learn/paths/create-machine-learn-models/
๐ Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
๐2โค1