Millionlights University

Welcome Students to the 8 week Self Paced course on Datascience

What is Included in your Course

Know your Mentor - Dr Manish Jain

Session 1 - Introduction Session

Session 2 with Dr Manish

1. Introduction to Python - Python Versions and Installing

Environment Variables

Executing Python from the command line

IDLE

Editing Python files

Python Documentation Getting Help

Dynamic Types

Python Reserved words

Naming Conventions

2. Basic Python Syntax - Comments

Comments - presentation

String Values

String Values - presentation

String methods

String methods - presentation

String Operators and the format method

String Operators - presentation

Numeric Data types

Numeric Data types - presentation

Conversion Functions

Simple Input and Output

The Print Function

3. Language Components Indenting requirements - The If statement

Relational Operators

Relational Operators - presentation

Logical Operators

Logical Operators - presentation

Bitwise Operators

Bitwise Operators - presentation

Looping and Branching

Looping and Branching - presentation

4. Collection Lists - Lists

Lists - presentation

Tuples, Sets, Dictionaries, Sorting Dictionaries

Tuples, Sets, Dictionaries - presentation

Copying Collections

Exercise - Bit wise operators

Exercise - Looping

Practice Exercises

Session 3 with Dr Manish - Recorded Sessions

1. Introduction to NumPy - NumPy Standard Data Types

Session 4

Basics of NumPy Arrays

NumPy Array Attributes

Array Indexing : accessing single elements

Array Slicing : accessing sub arrays

Reshaping of Arrays

Array Concatenation and Splitting

Computation on NumPy arrays : universal functions

Practice Exercises

Numpy Tutorial

Session 5

2. Data Manipulation with Pandas - Installing and using Pandas

Data Selection in Dataframe

Reading Data using Pandas

Missing Values in Dataframes

Dataframe Methods

Graphics to Explore the Data

Dataframe Attributes

Basic Descriptive Statistics

Aggregation functions in Pandas

Pandas

Pandas new

Practice Exercises

Session 6

3. Visualization with Matplotlib - Bar plot

Histogram

Pie chart

Scatter plot

Contour plot (Level plot)

Box plot

Session 7

Violin plot

Heat map

Session 8

Data Visualization with Seaboarn

Plot and Chart

Practice Exercises

Session 9

1. Linear Regression - Simple Linear Regression

Polynomial Linear Regression

Cost Function of Linear Regression

Understanding Linear Regression using Matrix

2. Logistic Regression - Cost Function and Mathematical Foundation

Session 10

Implementing Logistic Regression

Logistic Regression : Use - cases

3. Machine Learning

Session 11

4. Naive Bayes classification - Bayes Theorem and Mathematical Foundation

Types of Naive Bayes

When to use Naive Bayes

Naive Bayes Classification

Use cases of Naive Bayes

Session 12

4. Decision Trees and Random Forests - Understanding Decision Trees

Session 13

Random Forest Regression

Ensembles of Estimators : Random Forests

Application of Random Forest

Practice Exercise

Session 14

1. Building Recommender System - Recommendation Systems

Need of Recommendation Systems

Use case of Recommendation Systems

Types of Recommendations Systems

Data Acquisition

User-Based Collaborative Filtering

Collaborative Filtering Algorithm

2. Genetic Algorithms - Introduction to Neural Networks

Applications of Neural Networks

Artificial Neural Networks

Implementation steps in Neural Networks

Activation Functions

3. Natural Language Processing - Text Mining

Tokenization

Stemming

Lemmatization

Session 15

Session 16

Chunking

Session 17

Session 18

1. K-Nearest Neighbors - Introduction to KNN

KNN Feature Weights

Feature Normalization

Session 19

KNN Classification

Session 20

2. Support Vector Machines - Optimization Problem

Hyperplane

Session 21

3. Neural Networks in Projects - Syntax for Neural Networks

Session 22