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Wednesday, December 9, 2020

Machine Learning with Python Training (beginner to advanced) [Free 100% off premium Udemy course coupon code]

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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?

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

Requirements and What you should know?

  • Enthusiasm and determination to make your mark on the world!

Who is this course for?

  • Machine Learning Software Engineers & Developers
  • AI Specialists & Consultants
  • Python Engineers Machine Learning Ai Data Science
  • Principal Machine Learning Engineers
  • Machine Learning Analysts
  • Machine Learning Scientists
  • Python Programmers & Developers
  • Data Scientists and Senior Data Scientists
  • Beginners and newbies aspiring for a career in Data Science and Machine Learning
  • Machine Learning Researchers & Enthusiasts
  • Computer Vision Machine Learning Engineers
  • Anyone interested to learn Data Science, Machine Learning programming through Python
  • Data, Analytics, AI Consultants & Analysts

What is this course about?

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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