Udemy Machine Learning with Python Training (beginner to advanced) | 24 hours

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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
  • 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

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
Machine Learning with Python Training (beginner to advanced) | Udemy
 

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