Start your Data Science journey by learning the core data science concepts, along with applying them in exclusive real-time use cases, from industry experts and experienced faculties

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#### Course Overview

Learn essential data science techniques using Python to analyze the data and help make your organization take important decisions. Explore popular classification, regression, clustering, etc. algorithms using many hands-on exercises. By the end of this module, you will be able to transform your theoretical knowledge into practical by looking at real-life business scenarios.

#### Prerequisite

Students must have completed their graduations

Basic Python programming skills needed

#### Course Content

**Coding is for Everyone**

Installing Anaconda

Learning Coding Practices

Python Variables & Data Types

Python Operators

Python Conditional Statements & Loops

Python Strings

Python Data Structures

Python UDFs & Lambda Functions

NumPy

Pandas Series

Pandas DataFrames

Data Visualization

Regular Expressions

Exploratory Data Analysis with Python

**Introduction to Data Science, ML, AI**

What is Data Science, ML, AI

Why Data Science, ML, AI

Business Case Studies

How to prepare for a career switch

Freshers: How to study for interviews

Experienced: How to study for interviews

**Statistics without Stress**

Why do we need statistics for Data Science?

Measures of Central Tendencies

Measures of Dispersion

Central Limit Theorem

How do I apply these concepts?

Commonly used distributions – Binomial, Poisson, Geometric and Normal

Probability Theory

Why do we need probability in Data Science?

Naive Bayesian

Hypothesis Testing

Can I apply them in real life? Yes.

**Statistical Modeling**

Linear Regression

Understanding how Linear Regression (OLS) works

Easy-to-Understand Maths for Linear Regression

Data Encoding – Why Encoding? How to Encode? What Encoding?

Assumption of Normality

Assumption of Autocorrelation

Assumption of Heteroskedasticity

Interpreting A-Z of Linear Regression Output

Feature Engineering

Understanding Performance Metrics

Hands-on: Basic Case Study

Hands-on: Advanced Case Study demonstrating real-life challenges

Logistic Regression

Understanding how Logistic Regression (MLE) works

Easy-to-Understand Maths for Logistic Regression

Interpreting A-Z of Logistic Regression Output

Understanding Performance Metrics

Hands-on: Basic Case Study

Hands-on: Advanced Case Study demonstrating real-life challenges

**Introduction to Machine Learning**

Types of Learning

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

How do I know which algorithm to apply?

Understanding Bias & Variance

Understanding Underfitting & Overfitting

How do I overcome Bias & Variance in real projects?

Understanding Loss Functions for different problems

Understanding & fine-tuning Hyperparameters

Understanding Cross-Validation

Optimization Techniques

Local Optimization vs Global Optimization

Stochastic Gradient Descent

Batch & Mini-Batch Gradient Descent

Momentum, AdaGrad, RMSProp

ADAM Optimization

Hands-on: Revisiting Linear Regression with Optimization Algorithms

Hands-on: Revisiting Logistic Regression with Optimization Algorithms

Why do I need Optimization Techniques?

**Predictive Modeling with ML Algorithms**

Decision Tree for Supervised Learning Problems

Understanding how Decision Tree works

Easy-to-Understand Maths for Decision Tree

Understanding Gini Index and Information Entropy

Hands-on: Case Study using Decision Tree Algorithm

Hands-on: Overcoming Challenges in Decision Trees

Practical Key Takeaways

K-Nearest Neighbour

Understanding how KNN works

Understanding the distance metrics

Easy-to-Understand Maths for KNN

Determining when can we not use KNN appropriately

Support Vector Machines

Understanding how SVM works

Easy-to-Understand Maths for SVM

Understanding when & how SVM outperforms other algorithms

Understanding SVM Kernels

Hands-on: Case Study with SVM & SVM Kernels

Understanding when do you not need a kernel

Understanding which kernel to use

**Unsupervised Learning**

K-Means Clustering

Revisiting Distance Metrics

Understanding how K-Means Clustering works

Easy-to-Understand Maths for K-Means Clustering

Hands-on: Clustering using K-Means Algorithm

Practical Applications

Hierarchical Clustering

Understanding how Hierarchical Clustering works

Easy-to-Understand Maths for Hierarchical Clustering

Hands-on: Clustering using Hierarchical Algorithm

Practical Applications

Apriori Algorithm

Understanding how Apriori Algorithm works

Easy-to-Understand Maths for Apriori Algorithms

Understanding Metrics for Association Rules

Hands-on: Market Basket Analysis using Apriori Algorithm

Practical Applications

**Ensemble Machine Learning**

Random Forest

Understanding how Random Forest works

Why Random Forest?

Random Forest Hyperparameters

Fine-tuning Hyperparameters using Grid-Search

Hands-on: Case Study

Boosting Algorithms

Understanding how Boosting Algorithms works

Easy-to-Understand Maths behind Boosting Algorithms

Deep dive into Adaboost Algorithm

Deep dive in Gradient Boosting Method

Hands-on: Case Study on Adaboost & gradient Boosting Method

Heterogeneous Ensembles

Stack Generalization

**Neural Networks**

Artificial Neural Networks

How ANN Works

Easy-to-Understand Maths behind ANN

ANN Hyperparameters

ANN from scratch

Hands-on: Case Study on ANN

**Deep Learning**

Convolutional Neural Networks

Convolution network vs plain neural network

Convolution

Locally Connected Layer

Transitional Invariance

Convolutions (Discrete 1D)

Spatial dimension – convolutions

Convolution- backward

Recurrent Neural Networks

What are RNNs?

Forward pass in RNN

Backward pass in RNN

Vanishing and exploding gradient problem

Different types of RNN architecture

Bi-directional RNNs

Advantages and disadvantages of RNN

Application area of RNN

**Entering into the world of AI**

Natural Language Processing

NLP Foundation

Cleaning Text Manually

Cleaning Text using NLTK

Preparing Text with Scikit-Learn

Preparing Text with Keras

Bag of Words

Basic Neural Model for Sentiment Analysis

The n-gram CNN Model for Sentiment Analysis

Word Embeddings

Text Classification using CNN

Language Modeling

Develop a Neural Language Model for Text generation

Image Captioning using Pre-Trained Models

Computer Vision

Introduction to Computer Vision

Working with Images

Manipulating Images with PIL

Manipulating Images with Keras

Scaling Images with Keras

Image Augmentation

Manipulating & Ordering Image Channels

How Convolutional Neural works

Using Filter, Padding, Stride, Pooling

ImageNet Large Scale Visual Recognition

Milestone Model Architectures Implementations

Managing Model Complexity using 1X1 Convolutions

Using Pre-Trained Models, Transfer Learning

Image Classification

Object Detection

Face Recognition

**Model Deployments**

Understanding Git

ML Model Persistence

ML Model Deployments using Python frameworks

ML Model Deployments using Cloud Platform

**Building Models in distributed computing**

Understanding Big Data Ecosystem

Learning PySpark

Building ML Models using PySpark

#### Faculty Members

Senior Data Scientist, Ex-Maersk

Adjunct Faculty, Great Learning, Chennai and BML Munjal University, Gurgaon

With over 20 years of experience, Dipayan has a wealth of knowledge under his belt when it comes to analytics in real-world situations. Prior to being an independent consultant and a mentor in the Data Science, Machine Learning (ML), and AI space with various organizations, universities, and educational institutions, he served in the capacity of a senior data scientist with various Fortune 500 companies. He also holds a Masters in Economics.

Over the last decade, he has focused his analytics expertise in the fields of Data Science, ML, and AI as he has found how passion, technology, and analytics can be combined to create products that are essential in today’s business environment. Dipayan takes a keen interest in the mathematics behind the ML techniques.

While consulting for corporations on their Data Science and Analytics requirements, he is also a current content developer, mentor, and trainer in the Data Science, ML, and AI space with premier institutions such as Great Learning, Amity University, BML Munjal University.

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### Course Fee

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Contact Details

#### Course Certificate

Participants get a course completion certificate upon passing an examination

The examination is conducted by Algoritmo Lab

Rescheduling of examination is allowed

The course fee is inclusive of the examination fee