Course Curriculum

Learning Path

  • 1

    Welcome to the Course

    • Welcome Students to the 8 week Self Paced course on Datascience

    • What is Included in your Course

    • Know your Mentor - Dr Manish Jain

  • 2

    Week 1 : Python Programming

    • 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

  • 3

    Week 2 : Python for Data Science

    • 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

  • 4

    Week 3 : Machine Learning with Python

    • 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

  • 5

    Week 4 : Machine Learning with Python - 2

    • 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

  • 6

    Week 5 : Machine Learning with Python Project

    • 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

  • 7

    Week 6 : Create No-code Predictive Models with Azure Machine Learning

    • Session 23

    • 1. Introduction with Azure ML Studio - Azure Machine Learning Studio

    • Session 24

    • 2. Linear Regression with Azure ML Studio - Linear Regression

    • Session 25

    • Session 26

    • 3. Logistic Regression with Azure ML Studio - Two-Class Logistic Regression

    • Bayesian Linear Regression

    • Session 27

    • Multiclass Logistic Regression

    • Session 28

  • 8

    Week 7 : Perform Data Science and Azure Data Bricks

    • Session 29

    • 1. Decision Tree with Azure ML Studio - Boosted Decision Tree Regression

    • Two-Class Boosted Decision Tree

    • Session 30

    • 2. Random Forest with Azure ML Studio - Decision Forest Regression

    • Fast Forest Quantile Regression

    • Session 31

    • Multiclass Decision Forest

    • Session 32

    • Two-Class Decision Jungle

    • Session 33

    • Multiclass Decision Jungle

    • Session 34

  • 9

    Week 8 : Build AI Solutions with Azure Machine Learning

    • Session 35

    • 1. KNN with Azure ML Studio - K-means Clustering

    • Session 36

    • 2. SVM with Azure ML Studio - Introduction

    • Session 37

    • Convert to SVMLight

    • Session 38

    • 3. Neural Network with Azure ML Studio

    • Session 39

    • 4. R-Programming

    • Session 40

Our Instructor

Senior Mentor for the Course

Senior Mentor

Dr Manish Jain

Industry 4.0 | Blockchain | IOT | ML | Data Science | BigData | AI | Cloud Computing | Hadoop| Deep Learning | Data Science Trainer
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