Udemy Courses : Machine Learning with Python Training (beginner to advanced)
Machine Learning with Python - Course Syllabus
1. Introduction to Machine Learning
What is Machine Learning?
Need for Machine Learning
Why & When to Make Machines Learn?
Challenges in Machines Learning
Application of Machine Learning
2. Types of Machine Learning
Types of Machine Learning
a) Supervised learning
b) Unsupervised learning
c) Reinforcement learning
Difference between Supervised and Unsupervised learning
3. Components of Python ML Ecosystem
Using Pre-packaged Python Distribution: Anaconda
4. Regression Analysis (Part-I)
Examples on Linear Regression
scikit-learn library to implement simple linear regression
5. Regression Analysis (Part-II)
Multiple Linear Regression
Examples on Multiple Linear Regression
Examples on Polynomial Regression
6. Classification (Part-I)
What is Classification
Classification Terminologies in Machine Learning
Types of Learner in Classification
Example on Logistic Regression
7. Classification (Part-II)
What is KNN?
How does the KNN algorithm work?
How do you decide the number of neighbors in KNN?
Implementation of KNN classifier
What is a Decision Tree?
Implementation of Decision Tree
SVM and its implementation
8. Clustering (Part-I)
What is Clustering?
Applications of Clustering
How does K-Means Clustering work?
K-Means Clustering algorithm example
9. Clustering (Part-II)
Agglomerative Hierarchical clustering and how does it work
Woking of Dendrogram in Hierarchical clustering
Implementation of Agglomerative Hierarchical Clustering
10. Association Rule Learning
Association Rule Learning
Working of Apriori algorithm
Implementation of Apriori algorithm
11. Recommender Systems
Introduction to Recommender Systems
How Content-based Filtering work
Implementation of Movie Recommender System
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