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