/AI-ML

Primary LanguageJupyter Notebook

AI and Machine Learning Guide

This repository is a comprehensive guide for anyone preparing for AI, machine learning, and data science interviews. It includes a curated list of Medium articles that cover a wide range of topics, from the basics of machine learning to advanced concepts. These resources are designed to help you understand and master key concepts that are essential for interviews and practical applications. Additionally, the projects in this repository provide practical experience to solidify your understanding of these concepts.

Table of Contents

  1. What is Machine Learning?
  2. Types of Machine Learning Techniques. Clearly Explained.
  3. Why Python?
  4. Linear Regression
  5. Multiple Regression
  6. Overfitting VS Underfitting
  7. Exploring the Power of Cluster Analysis
  8. Data Science Revolution: Elevating Insights with ChatGPT
  9. Naive Bayes
  10. Tokenization and Vectorization
  11. Balancing the Data
  12. Why is Linear Algebra Useful?
  13. What Does a Neural Network Consist Of?
  14. How Machine Learning Models are Trained?
  15. Decoding the Mysteries of Machine Learning
  16. What is Objective Function?
  17. TensorFlow VS Scikit-Learn
  18. What Does A Deep Neural Network Consist of?
  19. Parameters VS Hyperparameters
  20. What is Activation Function?
  21. How Computers Actually Learn?
  22. Why Overfitting is Tricky?
  23. What is Validation?
  24. N-Fold Cross-Validation
  25. How to Know When to Stop Your Model From Being Trained?
  26. The Impact of Learning Rates
  27. How Computers See?
  28. How Do Computers Actually See?
  29. How Machine Learning Models Actually Remember?
  30. GANs: The Picasso of AI or Just Really Good Copycats?

Articles

  1. What is Machine Learning?
  2. Types of Machine Learning Techniques.
  3. Why Python?
  4. Linear Regression
  5. Multiple Regression
  6. Overfitting VS Underfitting
  7. Exploring the Power of Cluster Analysis
  8. Data Science Revolution: Elevating Insights with ChatGPT
  9. Naive Bayes
  10. Tokenization and Vectorization
  11. Balancing the Data
  12. Why is Linear Algebra Useful?
  13. What Does a Neural Network Consist Of?
  14. How Machine Learning Models are Trained?
  15. Decoding the Mysteries of Machine Learning
  16. What is Objective Function?
  17. TensorFlow VS Scikit-Learn
  18. What Does A Deep Neural Network Consist of?
  19. Parameters VS Hyperparameters
  20. What is Activation Function?
  21. How Computers Actually Learn?
  22. Why Overfitting is Tricky?
  23. What is Validation?
  24. N-Fold Cross-Validation
  25. How to Know When to Stop Your Model From Being Trained?
  26. The Impact of Learning Rates
  27. How Computers See?
  28. How Do Computers Actually See?
  29. How Machine Learning Models Actually Remember?
  30. GANs: The Picasso of AI or Just Really Good Copycats?

How This Guide Helps

This guide serves as an invaluable resource for:

  • Interview Preparation: Gain a strong grasp of AI, machine learning, and data science fundamentals to ace your interviews.
  • Skill Enhancement: Deepen your knowledge and skills in various machine learning techniques and tools.
  • Practical Insights: Understand real-world applications and implications of machine learning models and algorithms.
  • Advanced Topics: Explore cutting-edge topics such as GANs, deep learning, and the latest advancements in AI.

By following the articles listed and checking off the topics as you go, you'll be well-prepared to tackle both technical interviews and practical challenges in the field of AI and machine learning. The projects in this repository provide hands-on experience and practical application of the concepts discussed, enhancing your learning process and preparing you for real-world scenarios.