Machine Learning For Data Science With Python By Spotle

[LeeAndro]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 39 lectures (5h 30m) | Size: 1.72 GB

Machine learning, python and data science have become key industry drivers in the global job and opportunity market.

This Spotle masterclass by industry and acad leaders is for people who want to build careers in data science

Artificial Intelligence and machine learning fundamentals

Types of machine learning

Supervised and unsupervised machine learning and their differences

Application of supervised and unsupervised machine learning

Semi-supervised and reinforcement learning

Linear regression

Fitting linear regression model to data

Model complexity and bias-variance trade-off in linear regression

Variable selection in linear regression

Statistical inference in linear regression

Multicollinearity

Measures of accuracy in linear regression

Linear regression in python

Logistic regression

Likelihood estimation

Statistical inference in logistic regression

Measure of accuracy in logistic regression

Logistic regression in python

Decision tree

Decision tree, impurity gain ratio

Decision tree, numerical attributes

Decision tree in python

Regression tree

Regression tree in python

Cluster analysis

Features of cluster analysis

k-Means clustering

k-Means clustering in python

Hierarchical clustering

Hierarchical clustering case studies

You will need to have a computer or a mobile handset with an internet connection

This course with mix of lectures from industry experts and Ivy League acads will help students, recent graduates and young professionals learn machine learning and its applications in business scenarios using python programming language.

In this course you will learn:

1. Artificial Intelligence and machine learning fundamentals

2. Types of machine learning

3. Supervised and unsupervised machine learning and their differences

4. Application of supervised and unsupervised machine learning

5. Semi-supervised and reinforcement learning

6. Linear regression

7. Fitting linear regression model to data

8. Model complexity and bias-variance trade-off in linear regression

9. Variable selection in linear regression

10. Statistical inference in linear regression

11. Multicollinearity

12. Measures of accuracy in linear regression

13. Linear regression in python

14. Logistic regression

15. Likelihood estimation

16. Statistical inference in logistic regression

17. Measure of accuracy in logistic regression

18. Logistic regression in python

19. Decision tree

20. Decision tree, impurity gain ratio

21. Decision tree, numerical attributes

22. Decision tree in python

23. Regression tree

24. Regression tree in python

25. Cluster analysis

26. Features of cluster analysis

27. k-Means clustering

28. k-Means clustering in python

29. Hierarchical clustering

30. Hierarchical clustering case studies

What is supervised learning?

Let's say I have labeled fruits and I kept them in separate baskets. So you have separate baskets for yellow banana, golden pineapple, black grapes and so on. Now if I give you a golden pineapple you know exactly what it is and in which basket you need to keep it. So, I am helping you classify fruits by previously labeled and classified fruits.

What essentially is happening here is helping you learn about fruits which are already labeled. You know the characteristics and labels based on which they are separated into different baskets. The labeled fruits help you train your brain about their respective correct baskets. Now, for each new fruit you can put them into its respective basket. When machines learn in this way this is called supervised learning. Supervised learning is a learning in which we teach or train the machine using data which are properly or rather correctly labeled.

What is unsupervised learning?

Unsupervised learning is the learning of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.

We will take an example to understand the unsupervised learning process. Let's say, you are traveling to . There are many animals, snakes, birds and insects that you have never ever seen in your life. Now, in there you see a new small bird that you have never seen before. No one tells you that it is a bird not a large size insect. You can still make out that it is a bird because it has feathers, it has beak, it can fly etc. No one has taught you about it by labeling it as a bird but you learn from unlabeled data. This is unsupervised learning. The phases of learning are pretty simple. You have input data, you have your algorithm that categorizes, and then you have the output.

Anyone with an interest in a rewarding career in Data Science



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