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Machine Learning with Python Training (beginner to advanced)

via Udemy

Overview

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

Taught by

Uplatz Training

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