[25 HOUR] Machine Learning with Python Training (beginner to advanced)

Short Description

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

This course includes:

• 24 hours on-demand video
• 19 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of completion
• 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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