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*Deep dive into Machine Learning with Python Programming. Implement practical scenarios & a project on Recommender System*

**Students : ** 10,197 students

**Duration:** 24 hours on-demand video

**Ratings: **0.0 out of** 5.0** ( 770 ratings)

**Certificate of completion**

Created by Uplatz Training

**Language: **English

## 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