ExpiredMachine 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
• 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