Ramgopal Prajapat:

Learnings and Views

Introduction to Machine Learning Methods

Course Curriculum

We have coverred the key concepts using examples and scenarios. This is an introduction course, we are happy to conduct a more detailed sessions on each of these topics.

Session Title Details Files & Resources
1 Machine Learning: Introduction

Machine Learning Method Types

Machine Learning Project Life Cycle

Key Concepts and Terminologies

Scenarios: Car Price Estimation

Presentation
2 Python for Data Science

Set up and getting started with Python

Basic Operations in Python

Pandas DataFrame and Series

Reading different sources of data

Right click and save code and JSON file

Code

Grocery(CSV)

Reviews(JSON File)

SQL DB

HTML Page Link

3 Data Management in Python

Data Integration

Data Summarization

Basic Graphics with Python

Code

4 Exploratory Data Analysis

Exploratory Data Analysis(EDA)

Scenario:Retail Shop

Code Data
5 Unsupervised Machine Learning Methods

K Means Clustering

Scenario:Retail Transaction Segmentation

Code

Data

Original Source
6 Supervised Methods: Regression

Multiple Regression

Scenario:House Price Estimation

Code

Data

Original Source

7 Supervised Methods: Classification

Overview to 3 Machine Learning Algorithms – Logistic Regression, KNN and SVM

Machine Learning Models using Logistic Regression

Machine Learning Models using K Nearest Neighbors

Machine Learning Models using Support Vector Machine

Scenario: German Credit

Code Data
8 Supervised Methods: Classification

Tree Based Supervised Machine Learning Methods

Decision Tree: Concepts and Hands on session using Python

Random Forest: Concepts and Hands on session using Python

GBM: Concepts and Hands on session using Python

Scenario: German Credit

Code Data