Machine Learning and Data Science with Python Online Course

Machine Learning and Data Science with Python Online Course

Artificial Intelligence, Machine Learning, and Deep Learning are some of the most frequently used — yet often misunderstood — terms in today’s tech landscape. While AI is a broad field aimed at creating intelligent machines, Machine Learning and Neural Networks are specialized areas within it. This course focuses specifically on Machine Learning, guiding you through the process of training models and preparing them for accurate predictions.

You’ll use Python, a powerful and widely adopted language in the data science community. With its rich ecosystem of libraries and tools, Python allows you to perform complex data analysis and predictive modeling with minimal code, making it an ideal language for both beginners and experienced developers.

Machine Learning and Data Science are among the most in-demand and high-paying fields in tech today. By completing this course, you’ll gain a strong foundation in essential ML concepts, positioning yourself for success in a data-driven career.

Who should take this Course?

The Machine Learning and Data Science with Python Online Course is ideal for aspiring data scientists, machine learning engineers, analysts, and software developers who want to build intelligent data-driven applications using Python. It’s also suitable for students and professionals working in fields like finance, healthcare, marketing, and research. Prior knowledge of Python programming, basic statistics, and linear algebra is recommended for a smooth and effective learning experience.

Course Curriculum 

1. Introduction to Machine Learning
2. System and Environment preparation
3. Learn Basics of python
4. Learn Basics of NumPy
5. Learn Basics of Matplotlib
6. Learn Basics of Pandas
7. Understanding the CSV data file
8. Load and Read CSV data file
9. Dataset Summary
10. Dataset Visualization
11. Data Preparation
12. Feature Selection
13. Refresher Session - The Mechanism of Re-sampling, Training and Testing
14. Algorithm Evaluation Techniques
15. Algorithm Evaluation Metrics
16. Classification Algorithm Spot Check - Logistic Regression
17. Classification Algorithm Spot Check - Linear Discriminant Analysis
18. Classification Algorithm Spot Check - K-Nearest Neighbors
19. Classification Algorithm Spot Check - Naive Bayes
20. Classification Algorithm Spot Check – CART
21. Classification Algorithm Spot Check - Support Vector Machines
22. Regression Algorithm Spot Check - Linear Regression
23. Regression Algorithm Spot Check - Ridge Regression
24. Regression Algorithm Spot Check - LASSO Linear Regression
25. Regression Algorithm Spot Check - Elastic Net Regression
26. Regression Algorithm Spot Check - K-Nearest Neighbors
27. Regression Algorithm Spot Check – CART
28. Regression Algorithm Spot Check - Support Vector Machines (SVM)
29. Compare Algorithms - Part 1: Choosing the best Machine Learning Model
30. Compare Algorithms - Part 2: Choosing the best Machine Learning Model
31. Pipelines: Data Preparation and Data Modelling
32. Pipelines: Feature Selection and Data Modelling
33. Performance Improvement: Ensembles – Voting
34. Performance Improvement: Ensembles – Bagging
35. Performance Improvement: Ensembles – Boosting
36. Performance Improvement: Parameter Tuning using Grid Search
37. Performance Improvement: Parameter Tuning using Random Search
38. Export, Save and Load Machine Learning Models: Pickle
39. Export, Save and Load Machine Learning Models: Joblib
40. Export, Save and Load Machine Learning Models Joblib
41. Finalizing a Model - Introduction and Steps
42. Finalizing a Classification Model - The Pima Indian Diabetes Dataset
43. Quick Session: Imbalanced Data Set - Issue Overview and Steps
44. Iris Dataset: Finalizing Multi-Class Dataset
45. Finalizing a Regression Model - The Boston Housing Price Dataset
46. Real-time Predictions: Using the Pima Indian Diabetes Classification Model
47. Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset
48. Real-time Predictions: Using the Boston Housing Regression Model