𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝘃𝘀. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘃𝘀. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝘃𝘀. 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁

Think of them as data detectives.
→ 𝐅𝐨𝐜𝐮𝐬: Identifying patterns and building predictive models.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Machine learning, statistics, Python/R.
→ 𝐓𝐨𝐨𝐥𝐬: Jupyter Notebooks, TensorFlow, PyTorch.
→ 𝐆𝐨𝐚𝐥: Extract actionable insights from raw data.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Creating a recommendation system like Netflix.

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿

The architects of data infrastructure.
→ 𝐅𝐨𝐜𝐮𝐬: Developing data pipelines, storage systems, and infrastructure. → 𝐒𝐤𝐢𝐥𝐥𝐬: SQL, Big Data technologies (Hadoop, Spark), cloud platforms.
→ 𝐓𝐨𝐨𝐥𝐬: Airflow, Kafka, Snowflake.
→ 𝐆𝐨𝐚𝐥: Ensure seamless data flow across the organization.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Designing a pipeline to handle millions of transactions in real-time.

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁

Data storytellers.
→ 𝐅𝐨𝐜𝐮𝐬: Creating visualizations, dashboards, and reports.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Excel, Tableau, SQL.
→ 𝐓𝐨𝐨𝐥𝐬: Power BI, Looker, Google Sheets.
→ 𝐆𝐨𝐚𝐥: Help businesses make data-driven decisions.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Analyzing campaign data to optimize marketing strategies.

𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿

The connectors between data science and software engineering.
→ 𝐅𝐨𝐜𝐮𝐬: Deploying machine learning models into production.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Python, APIs, cloud services (AWS, Azure).
→ 𝐓𝐨𝐨𝐥𝐬: Kubernetes, Docker, FastAPI.
→ 𝐆𝐨𝐚𝐥: Make models scalable and ready for real-world applications. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Deploying a fraud detection model for a bank.

𝗪𝗵𝗮𝘁 𝗣𝗮𝘁𝗵 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗖𝗵𝗼𝗼𝘀𝗲?

☑ Love solving complex problems?
→ Data Scientist
☑ Enjoy working with systems and Big Data?
→ Data Engineer
☑ Passionate about visual storytelling?
→ Data Analyst
☑ Excited to scale AI systems?
→ ML Engineer

Each role is crucial and in demand—choose based on your strengths and career aspirations.

What’s 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 👍👍

Join our WhatsApp channel for more Data Science Resources 👇👇
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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 Bayes’ theorem, prior and posterior distributions, and Bayesian networks.

Python Programming Resources
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If you want to get a job as a machine learning engineer, don’t 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:


𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - 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

𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - 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.

𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - 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/

𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.

𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
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.

𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
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 👍👍

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?

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

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Machine Learning Algorithms 👆

Data Science Tip💡

Always start with 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 before jumping into complex models.

• Understand Descriptive vs. Inferential Statistics: Descriptive summarizes; Inferential predicts. 

• Use the Empirical Rule (68-95-99.7) to grasp normal distribution probabilities.

• Apply standard deviation and variance to quantify data spread. 

• Leverage probability distributions like PMF, PDF, and CDF for modeling. 

• 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?

↳ Focus on 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: 

• Identify whether data follows a normal distribution using Q-Q plots. 

• Use visualizations like boxplots and histograms for a quick overview. 

• Incorporate parametric and non-parametric methods for density estimations. 

• Avoid misrepresentation by understanding skewness and kurtosis. 

• Validate results with statistical tests like Shapiro-Wilk for normality. 

See how much you improve 𝘆𝗼𝘂𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀.

Statistics Roadmap for Data Science!

Phase 1: Fundamentals of Statistics

1️⃣ Basic Concepts
-Introduction to Statistics
-Types of Data
-Descriptive Statistics

2️⃣ Probability
-Basic Probability
-Conditional Probability
-Probability Distributions

Phase 2: Intermediate Statistics

3️⃣ Inferential Statistics
-Sampling and Sampling Distributions
-Hypothesis Testing
-Confidence Intervals

4️⃣ Regression Analysis
-Linear Regression
-Diagnostics and Validation

Phase 3: Advanced Topics

5️⃣ Advanced Probability and Statistics
-Advanced Probability Distributions
-Bayesian Statistics

6️⃣ Multivariate Statistics
-Principal Component Analysis (PCA)
-Clustering

Phase 4: Statistical Learning and Machine Learning

7️⃣ Statistical Learning
-Introduction to Statistical Learning
-Supervised Learning
-Unsupervised Learning

Phase 5: Practical Application

8️⃣ Tools and Software
-Statistical Software (R, Python)
-Data Visualization (Matplotlib, Seaborn, ggplot2)

9️⃣ Projects and Case Studies
-Capstone Project
-Case Studies

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

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 Bayes’ theorem, prior and posterior distributions, and Bayesian networks.

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻:
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.  Here’s a breakdown of how outliers influence kNN:

𝗛𝗶𝗴𝗵 𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲
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.

𝗗𝗶𝘀𝘁𝗮𝗻𝗰𝗲 𝗠𝗲𝘁𝗿𝗶𝗰 𝗦𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗶𝘁𝘆
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 model’s ability to effectively measure "closeness" degrades.

𝗥𝗲𝗱𝘂𝗰𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝗻 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻/𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗮𝘀𝗸𝘀
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.

𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗜𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗗𝗶𝘀𝗽𝗿𝗼𝗽𝗼𝗿𝘁𝗶𝗼𝗻
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 👍👍

Complete Roadmap to learn Machine Learning and Artificial Intelligence
👇👇

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 👇

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 LEARNING👍👍

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 👍👍

Many data scientists don't know how to push ML models to production. Here's the recipe 👇

𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀

🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data
🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time
🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model
🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github
🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API
🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API
🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker
🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs
🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring

𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲

𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴

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.

𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁

Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.

𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻

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

𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁

Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.

𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴

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.

Essential Python Libraries for Data Analytics 😄👇

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 :)

Complete Roadmap to learn Data Science

1. Foundational Knowledge

Mathematics and Statistics

- Linear Algebra: Understand vectors, matrices, and tensor operations.
- Calculus: Learn about derivatives, integrals, and optimization techniques.
- Probability: Study probability distributions, Bayes' theorem, and expected values.
- Statistics: Focus on descriptive statistics, hypothesis testing, regression, and statistical significance.

Programming

- Python: Start with basic syntax, data structures, and OOP concepts. Libraries to learn: NumPy, pandas, matplotlib, seaborn.
- R: Get familiar with basic syntax and data manipulation (optional but useful).
- SQL: Understand database querying, joins, aggregations, and subqueries.

2. Core Data Science Concepts

Data Wrangling and Preprocessing

- Cleaning and preparing data for analysis.
- Handling missing data, outliers, and inconsistencies.
- Feature engineering and selection.

Data Visualization

- Tools: Matplotlib, seaborn, Plotly.
- Concepts: Types of plots, storytelling with data, interactive visualizations.

Machine Learning

- Supervised Learning: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors.
- Unsupervised Learning: K-means clustering, hierarchical clustering, PCA.
- Advanced Techniques: Ensemble methods, gradient boosting (XGBoost, LightGBM), neural networks.
- Model Evaluation: Train-test split, cross-validation, confusion matrix, ROC-AUC.


3. Advanced Topics

Deep Learning

- Frameworks: TensorFlow, Keras, PyTorch.
- Concepts: Neural networks, CNNs, RNNs, LSTMs, GANs.

Natural Language Processing (NLP)

- Basics: Text preprocessing, tokenization, stemming, lemmatization.
- Advanced: Sentiment analysis, topic modeling, word embeddings (Word2Vec, GloVe), transformers (BERT, GPT).

Big Data Technologies

- Frameworks: Hadoop, Spark.
- Databases: NoSQL databases (MongoDB, Cassandra).

4. Practical Experience

Projects

- Start with small datasets (Kaggle, UCI Machine Learning Repository).
- Progress to more complex projects involving real-world data.
- Work on end-to-end projects, from data collection to model deployment.

Competitions and Challenges

- Participate in Kaggle competitions.
- Engage in hackathons and coding challenges.

5. Soft Skills and Tools

Communication

- Learn to present findings clearly and concisely.
- Practice writing reports and creating dashboards (Tableau, Power BI).

Collaboration Tools

- Version Control: Git and GitHub.
- Project Management: JIRA, Trello.

6. Continuous Learning and Networking

Staying Updated

- Follow data science blogs, podcasts, and research papers.
- Join professional groups and forums (LinkedIn, Kaggle, Reddit, DataSimplifier).

7. Specialization

After gaining a broad understanding, you might want to specialize in areas such as:
- Data Engineering
- Business Analytics
- Computer Vision
- AI and Machine Learning Research

👨‍💻Websites to Practice Python

Python Basics 👇:
1. https://codingbat.com/python
2. https://www.hackerrank.com/
3. https://www.hackerearth.com/practice/

Practice Problems set :
4. https://projecteuler.net/archives
5. https://www.codeabbey.com/index/task_list
6. https://www.pythonchallenge.com/

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