IT IS FOR MACHINE LAERNING AND PYHTON

1. Machine Learning Basics and
Practical Applications
Understanding, Implementation, and
Optimization with Python

2. To understand the basics of
machine learning and its practical
applications.
• What is Machine Learning?

3. To understand the basics of
machine learning and its practical
applications.
• Types of Machine Learning: Supervised,
Unsupervised, Reinforcement

4. To understand the basics of
machine learning and its practical
applications.
• Machine Learning vs Traditional Programming

5. To understand the basics of
machine learning and its practical
applications.
• Real-world Applications of ML: Healthcare,
Finance, Retail, etc.

6. To understand the basics of
machine learning and its practical
applications.
• Key Concepts: Model, Training, Inference

7. To understand the basics of
machine learning and its practical
applications.
• Overview of Popular Algorithms: Linear
Regression, Decision Trees, KNN

8. To understand the basics of
machine learning and its practical
applications.
• Ethics in Machine Learning

9. To understand the basics of
machine learning and its practical
applications.
• Limitations and Challenges in ML

10. To gain hands-on experience with
Python-based ML tools and
libraries.
• Why Python for Machine Learning?

11. To gain hands-on experience with
Python-based ML tools and
libraries.
• Setting up Python Environment

12. To gain hands-on experience with
Python-based ML tools and
libraries.
• Using Jupyter Notebooks

13. To gain hands-on experience with
Python-based ML tools and
libraries.
• Popular Libraries: NumPy, Pandas, Scikit-learn,
Matplotlib, Seaborn

14. To gain hands-on experience with
Python-based ML tools and
libraries.
• Installing and Importing Libraries

15. To gain hands-on experience with
Python-based ML tools and
libraries.
• Dataset Loading and Exploration with Pandas

16. To gain hands-on experience with
Python-based ML tools and
libraries.
• Visualizing Data with Matplotlib and Seaborn

17. To gain hands-on experience with
Python-based ML tools and
libraries.
• Intro to Scikit-learn: Structure and Workflow

18. To build, train, and evaluate simple
machine learning models.
• Steps in Building an ML Model

19. To build, train, and evaluate simple
machine learning models.
• Loading a Dataset

20. To build, train, and evaluate simple
machine learning models.
• Splitting Data: Train-Test Split

21. To build, train, and evaluate simple
machine learning models.
• Choosing an Algorithm

22. To build, train, and evaluate simple
machine learning models.
• Training the Model

23. To build, train, and evaluate simple
machine learning models.
• Making Predictions

24. To build, train, and evaluate simple
machine learning models.
• Evaluating the Model: Accuracy, Confusion
Matrix, ROC-AUC

25. To build, train, and evaluate simple
machine learning models.
• Improving Model Performance

26. To explore data preprocessing,
feature selection, and model
optimization techniques.
• Why Preprocessing is Important?

27. To explore data preprocessing,
feature selection, and model
optimization techniques.
• Handling Missing Data

28. To explore data preprocessing,
feature selection, and model
optimization techniques.
• Encoding Categorical Variables

29. To explore data preprocessing,
feature selection, and model
optimization techniques.
• Feature Scaling: Normalization and
Standardization

30. To explore data preprocessing,
feature selection, and model
optimization techniques.
• Feature Engineering Basics

31. To explore data preprocessing,
feature selection, and model
optimization techniques.
• Feature Selection Techniques

32. To explore data preprocessing,
feature selection, and model
optimization techniques.
• Cross Validation for Reliable Evaluation

33. To explore data preprocessing,
feature selection, and model
optimization techniques.
• Hyperparameter Tuning: Grid Search and
Random Search

34. To explore data preprocessing,
feature selection, and model
optimization techniques.
• Pipeline Construction in Scikit-learn

35. To explore data preprocessing,
feature selection, and model
optimization techniques.
• Model Comparison and Selection