Machine_Learning_Basics_Presentation.pptx
- 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