Machine Learning with Python Complete Certification Training

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