This Spotle masterclass by industry and academic leaders is for people who want to build careers in data science
What you'll learn
- 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
Requirements
- You will need to have a computer or a mobile handset with an internet connection
Description
Machine learning, python and data science have become key industry drivers in the global job and opportunity market. This course with mix of lectures from industry experts and Ivy League academics 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 Amazon. 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.
Who this course is for:
- Anyone with an interest in a rewarding career in Data Science
