Machine Learning with Python Training (beginner to advanced)

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Requirements

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

Description

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

Who this course is for:

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