Machine Learning Full Course | Tutorial | Simplilearn [7 Hours]

Program Overview

This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning. It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let’s get started with Machine Learning.

Below topics are explained in this Machine Learning course for beginners:

1. Basics of Machine Learning – 01:46
2. Why Machine Learning – 09:18
3. What is Machine Learning – 13:25
4. Types of Machine Learning – 18:32
5. Supervised Learning – 18:44
6. Reinforcement Learning – 21:06
7. Supervised VS Unsupervised – 22:26
8. Linear Regression – 23:38
9. Introduction to Machine Learning – 25:08
10. Application of Linear Regression – 26:40
11. Understanding Linear Regression – 27:19
12. Regression Equation – 28:00
13. Multiple Linear Regression – 35:57
14. Logistic Regression – 55:45
15. What is Logistic Regression – 56:04
16. What is Linear Regression – 59:35
17. Comparing Linear & Logistic Regression – 01:05:28
18. What is K-Means Clustering – 01:26:20
19. How does K-Means Clustering work – 01:38:00
20. What is Decision Tree – 02:15:15
21. How does Decision Tree work – 02:25:15
22. Random Forest Tutorial – 02:39:56
23. Why Random Forest – 02:41:52
24. What is Random Forest – 02:43:21
25. How does Decision Tree work- 02:52:02
26. K-Nearest Neighbors Algorithm Tutorial – 03:22:02
27. Why KNN – 03:24:11
28. What is KNN – 03:24:24
29. How do we choose ‘K’ – 03:25:38
30. When do we use KNN – 03:27:37
31. Applications of Support Vector Machine – 03:48:31
32. Why Support Vector Machine – 03:48:55
33. What Support Vector Machine – 03:50:34
34. Advantages of Support Vector Machine – 03:54:54
35. What is Naive Bayes – 04:13:06
36. Where is Naive Bayes used – 04:17:45
37. Top 10 Application of Machine Learning – 04:54:48
38. How to become a Machine Learning Engineer – 04:59:46
39. Machine Learning Interview Questions – 05:09:03

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