Deep dive into Machine Learning with Python Programming. Implement practical scenarios & a project on Recommender System
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
- Summary
3. Components of Python ML Ecosystem
- Using Pre-packaged Python Distribution: Anaconda
- Jupyter Notebook
- NumPy
- Pandas
- Scikit-learn
4. Regression Analysis (Part-I)
- Regression Analysis
- Linear Regression
- 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
- Polynomial Regression
- Examples on Polynomial Regression
6. Classification (Part-I)
- What is Classification
- Classification Terminologies in Machine Learning
- Types of Learner in Classification
- Logistic Regression
- 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
- Clustering Algorithms
- K-Means Clustering
- How does K-Means Clustering work?
- K-Means Clustering algorithm example
9. Clustering (Part-II)
- Hierarchical Clustering
- 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
- Apriori algorithm
- Working of Apriori algorithm
- Implementation of Apriori algorithm
11. Recommender Systems
- Introduction to Recommender Systems
- Content-based Filtering
- How Content-based Filtering work
- Collaborative Filtering
- Implementation of Movie Recommender System
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