README.md

Machine Learning Sessions

Overview

This Bootcamp was organized by ACM JUIT from 9th April 2018 to 18th April, 2018 You can see the overview of this Bootcamp here

This Repository contains Codes, Handouts and Slides for the Session on Machine Learning by ACM JUIT. Feel free to use, edit and change whatever you feel like. All the rights are reserved with me for credits of materials. Just add credits whenever you need to reproduce.

Click here to Register for the BootCamp

That's all! :)

Clone Repository

In terminal paste this:

git clone https://github.com/akhilesh-k/Machine-Learning-Sessions.git

Download Repository

Download this Repository

Structure: 

 

Day 1

Introduction to Python Slides

Hands on Session on Python to stage everyone on a same level. Session will start with Python syntax.

• Operators
• Decision Statements
• Loops
• Methods
• Functions
• Lambda Expressions, Maps and Filter Function
• Objects and Classes
• Errors and Exception Handling

Day 2

Data Structures: Basics to Advanced

Hands on Sessions on Data Structures with Python. This session will include various Data Structures commonly used in Python.

• Data Types and Numbers
• Strings
• Lists
• Dictionaries
• Tuples
• Sets and Booleans
• Files

Day 3-4

Basic introduction to Python and Machine Learning

Overview of various Python libraries

We will leap to data preprocessing but initially we will give you breif overview of what's gonna be covered through the journey. We will provide Hands out for each of the libraries we will be using.

• NumPy 
• Pandas 
• scikit-learn 
• Matplotlib 

Kickstarting with data- Data Preprocessing

To enable our mind of though process of using which algorithm, we have to first analyze data. Here we will do all sort of Statistical processing, know the maths behind the process and then the practical implementation in Python.

• Importing Libraries 
• Importing Datasets 
• Statistical processing of Datasets 
• Data encoding 
• Splitting Datasets 
• Feature Scaling- Normalization and Standardization 

View the Resource here [Download Dataset]  

Day 5

Regression Algorithms and Hands on

• Simple Linear Regression [Download Dataset]
• Multiple Linear Regression [Download Dataset]
• Polynomial Regression [Download Dataset]
• Support vector regression ()[[Download Dataset]]
• Decision Tree Regression [Download Dataset]
• Random Forest Regression [Download Dataset]
• [Regularization Methods]

Day 6-7

Classification Algorithms and Hands on

• Logistic Regression [Download Dataset]
• K Nearest Neighbour [Download Dataset]
• Support Vector Machines [Download Dataset]
• Kernel SVM [Download Dataset]
• Naive Bayes [Download Dataset]
• Decision Tree Classification [Download Dataset]
• Random Forest Classification [Download Dataset]

Day 8

Clustering

• K-Means Clustering [Download Dataset]
• Hierarchical Clustering [Download Dataset]

Day 9

Natural Language Processing and Ensemble Methods

• NLP [Download Dataset]

Day 10

Neural Networks

• ANN [Download Dataset]
• CNN [Download Dataset]