Machine Learning with Python Training (beginner to advanced)

Deep dive into Machine Learning with Python Programming. Implement practical scenarios & a project on Recommender System

What you'll learn:
-> Deep dive into the world of Machine Learning (ML)
-> Apply Python for Machine Learning programs
-> Understand what is ML, need for ML, challenges & application of ML in real-life scenarios
-> Types of Machine Learning
-> Components of Python ML Ecosystem
-> Anaconda, Jupyter Notebook, NumPy, Pandas, Scikit-learn
-> Regression analysis
-> scikit-learn Library to implement Simple Linear Regression
-> Multiple Linear Regression and Polynomial Regression
-> Logistic Regression
-> What is Classification, Classification Terminologies in Machine Learning
-> What is KNN? How does the KNN algorithm work?
-> What is a Decision Tree and Implementation of Decision Tree
-> SVM and its implementation
-> What is Clustering and Applications of Clustering
-> Clustering Algorithms
-> K-Means Clustering and K-Means Clustering algorithm example
-> Hierarchical Clustering
-> Agglomerative Hierarchical clustering and how does it work
-> Woking of Dendrogram in Hierarchical clustering
-> Implementation of Agglomerative Hierarchical Clustering
-> Association Rule Learning
-> Apriori algorithm and Implementation of Apriori algorithm
-> Introduction to Recommender Systems
-> Content-based Filtering
-> Collaborative Filtering
-> Implementation of Movie 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