Analytics, Machine Learning and AI

• Introduction
• LOOKUP functions
• Pivot Tables
• WHATIF Analysis
• Dashboard Creation
• Recording Macros
• Advanced Visualizations- PIVOT Charts, Sparklines, Waterfall Charts
• Data Analysis ToolPak – Regression in Excel

Installing Anaconda & Basics of Python

• Introduction to programming languages
• Compiler vs Interpreter
• Getting Started With Python
• Introduction to jupyter Notebooks
• Identifiers, Keywords
• Print function
• Comment, Indentation
• Data Types Functions
• Understanding what are functions
• Defining and calling functions
• Local and global variables
• Different types of arguments
• Map,reduce,filter,lambda and recursive functions

Data Structures in Python

• Introduction
• Lists
• Tuples
• Sets
• Dictionaries
• Practice Exercise
• Summary

Operator Input and Output

• Different Arithmetic , logical and Relational operators
• Input, Output function
• Eval function
• Format Function

Control Flow

• If elif else statement
• For and while loops
• Break , continue and Pass statement
• List and dictionary comprehensions

Functions

• Understanding what are functions
• Defining and calling functions
• Local and global variables
• Different types of arguments
• Map,reduce,filter,lambda and recursive functions

File Handling

• Purpose of file handling
• Different function in file handling (open,read, write,close)
• Different modes (r,w,a,r+,w+,a+)
• With block

Exception Handling, OOPX & Regex

• What is exception handling
• Try, except, else and finally block
• Different types of Exception
• Concept of Oops
• Different functions in Regex
• Metacharacters in Regex

Data Sourcing

• Module Introduction
• Introduction to EDA
• Public and Private Data
• Private Data
• Public Data
• Web Scraping-I
• Web Scraping-II
• Summary

Data Cleaning

• Introduction
• Data Types
• Fixing the Rows and Columns
• Impute/Remove Missing Values
• Handling Outliers
• Standardising Values
• Fixing Invalid Values and Filter Data
• Practice Questions
• Summary

Univariate Analysis

• Introduction to Univariate Analysis
• Categorical Unordered Univariate Analysis
• Categorical Ordered Univariate Analysis
• Statistics on Numerical Features
• Graded Questions
• Summary

Bivariate and Multivariate Analysis

• Introduction
• Numeric – Numeric Analysis
• Correlation vs Causation
• Numerical – Categorical Analysis
• Categorical – Categorical Analysis
• Multivariate Analysis
• Graded Questions
• Summary
• Module Summary

Introduction to Simple Linear Regression

• Introduction to Simple Linear Regression
• Introduction to machine learning
• Regression line
• Best fit line
• Strength of simple linear regression

Simple linear regression in python

• Assumptions of simple linear regression
• Reading and understanding the data
• Hypothesis testing in linear regression
• Building a linear model
• Residue analysis and predictions
• Linear Regression using SKLearn

Multiple Linear Regression

• Motivation-when one variable is not enough
• Moving from SLR to MLR-new considerations
• Multi collinearity
• Dealing with categorical variables
• Model assessment in comparison
• Feature selection

Multiple Linear Regression in Python

• Reading and understanding the data
• Data preparation
• Initial steps
• Building the model I & II
• Residue analysis and predictions
• Variable selection using RFE

Industry Relevance of Linear Regression

• Linear regression revision
• Prediction versus projection
• Media company case study
• Exploratory data analysis
• Model building – I, II & III
• Assessing the model
• Interpreting the results

Univariate Logistic Regression

• Binary classification
• Sigmoid curve
• Finding the best fit sigmoid curve – I
• Finding the best fit sigmoid curve – II
• Odds and log Odds

Multivariate Logistic Regression – Model Building

• Multivariate Logistic Regression – Model Building
• Multivariate logistic regression with telecom churn example
• Data cleaning and preparation – I & II
• Building your first model
• Feature elimination using RFE
• Confusion metrics and accuracy
• Manual feature elimination

Multivariate Logistic Regression – Model Evaluation

• Multivariate Logistic Regression – Model Evaluation
• Metrics beyond accuracy-sensitivity and specificity
• Sensitivity and specificity in Python
• Understanding ROC curve
• ROC curve in python
• Finding the optimal threshold
• Model evaluation metrics – exercise
• Precision and recall
• Making predictions

Logistic Regression – Industry Applications – Part I

• Getting familiar with logistic regression
• Nuances of logistic regression-sample selection
• Nuances of logistic regression-segmentation
• Nuances of logistic impression-variable transformation-I, II & III
• Logistic Regression: Industry Applications – Part II
• Model evaluation – A second look
• Model validation and importance of stability
• Tracking of model performance over time

Logistic Regression – Industry Applications -Part II

• Commonly face challenges in implementation of logistic regression
• Model evaluation – A second look
• Model validation and importance of stability
• Tracking of model performance over time

Unsupervised Learning: Clustering

• Introduction to Clustering
• K Means Clustering
• Executing K Means in Python
• Hierarchical Clustering

Business Problem Solving

• Introduction to Business Problem Solving
• Case Study Demonstration churn example
• Practice Questions

Tree Models

• Introduction to Decision Trees
• Algorithms for Decision Tree Construction
• Hyperparameter Tuning in Decision Trees
• Ensembles and Random Forests

Time Series Forecasting – II (BA)

• Introduction to AR Models
• Building AR Models

Model Selection

• Principles of Model Selection
• Model Building and Evaluation

Time Series Forecasting – I (BA)

• Introduction to Time Series
• Smoothing Techniques

Introduction to NLP

• What is NLP?
• History and evolution of NLP
• Applications of NLP
• Challenges in NLP
• Overview of NLP pipeline
• Corpus and Corpus Linguistics

NLTK Toolkit

• Introduction to the NLTK toolkit
• Preprocessing text data with NLTK
• Basic NLP tasks using NLTK (e.g., Part-of-Speech Tagging, Named Entity Recognition)
• Stemming and Lemmatization
• WordNet in NLTK
• Chunking and Chinking
• Sentiment Analysis with NLTK

Tokenization and Topic Modeling

• Tokenization in NLP
• Bag-of-Words representation
• Topic Modeling with LDA
• Latent Semantic Analysis
• Word Embeddings

Sentiment Analysis Project

• Introduction to Sentiment Analysis
• Sentiment Analysis using supervised and unsupervised methods
• Building a Sentiment Analysis model with Python
• Evaluating Sentiment Analysis models

AI vs Deep Learning vs ML

• Introduction to Artificial Intelligence (AI),
• Machine Learning (ML) and Deep Learning (DL)
• Applications of AI, ML, and DL
• Differences between AI, ML and DL

The Concept of Neural Networks

• Introduction to Neural Networks
• Types of Neural Networks
• Layers in Neural Networks
• Activation Functions

Neural Networks – Feed-forward, Convolutional, Recurrent

• Feed-forward Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Applications of Neural Networks

Deep Learning Project

• Building a Deep Learning model with Python
• Image Classification with Convolutional Neural Networks
• Natural Language Processing with Recurrent Neural Networks
• Hyperparameter Tuning in Deep Learning