Master's Program in Data Science and Machine Learning

An 8-month program that involves everything you need to know about Data Science and Machine Learning with projects and challenges to help put things into practice

  • Domain : CSE
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Program Overview

In 2015, 3.26 billion people were actively using the Internet. In January 2021, it was around 4.66 billion. Every minute, around 500 hours of video content is being uploaded by the users on video streaming platforms. One million dollars is spent on online shopping and around 69,444 jobs are being applied by the users in job portals, every minute. Each post, like, share, comment, and swipe is extremely important for the business to grow. That's why Data Science is important.

Businesses make data-driven decisions to reach their potential consumers/audience to improve their business. Data Science uses Machine Learning techniques to analyze the data and make predictions based on it. For instance, OTT platforms showing movie or series recommendations, and shopping websites showing products that you may be interested in buying based on your previous purchases. 

In Data Science, we gather huge chunks of data, filter/group them based on our necessity/application, and use mathematics & statistics to see the pattern behind it. This way, we find the answers to the questions we need. Whereas, In Machine Learning, we use data to test and train our model. It is all about creating models that can learn from the given data and provide results or predictions based on recent trends.

Example: Recognizing fingerprints, Predicting stock prices, and self-driving cars.

Let's consider a scenario where Data Science and Machine Learning goes hand in hand. While working with data, a data scientist follows this 5 step process.

  1. Gathering data
  2. Pre-processing of data
  3. Analyzing the data
  4. Driving insights
  5. Decisions based on the insights

After this, a data scientist needs the help of a machine learning engineer to create a model based on this data to make predictions. These steps may vary based on the application and not always a Machine Learning solution is required to solve a Data Science problem.

Data Science and Machine Learning are blooming fields and their applications are numerous. The opportunities in this field have grown exponentially in recent years. The program “Masters in Data Science and Machine Learning” is designed for those who aspire to pursue their career and thrive in this domain. Through this course, you can get your basics strong and apply them in real-world applications. 

This Master’s program consists of 6 courses. The courses are as follows

  1. Math behind Machine Learning & Artificial Intelligence using Python
  2. SQL for DataScience
  3. Core and Advanced Python Programming
  4. Machine Learning for Electrical Engineers using Python
  5. Machine Learning & Artificial Intelligence for Mechanical, Civil & EE Engineers using Python
  6. Advanced Deep Learning

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List of courses in this program

1Math behind Machine Learning & Artificial Intelligence using Python

This module is specially designed for the Engineering students and graduates. It helps you leverage the basic maths to understand the basic concepts of Machine Learning and Artificial Intelligence.

In this module, you will learn about

  • Basic Concepts of Set theory, Trigonometric functions, Straight lines, A.M, G.M & H.M, and the Concepts of Vectors
  • Permutations and Combinations
  • Statistics and Probability
  • Likelihood (for Logical Regression)
  • Gradient Descent (for Linear & Logistic Regression)
  • Linear Algebra (for PCA)
  • Derivatives (for Neural Network)
  • Backpropagation (for Deep Learning) 

In this course, you will be working on projects related to Logistic Regression and Gradient Descent.

2SQL for DataScience

This module will cover the basics of SQL and how to perform data analysis on large datasets. 

In this module, you will learn about

  • Creating, Inserting, Updating and Querying of Relational Databases.
  • Method to understand Data Patterns.
  • Data cleaning and Computation.
  • Using SQL for Data Analysis & Data Visualization

In this module, you will extensively work on softwares like SQL & Relational Database Tools, which is widely used in the industry for database storage. It also helps students who are willing to pursue master’s or Phd in Data Analytics, Data Science and related fields.

3Core and Advanced Python Programming

Recently, Python has become one of the most popular programming languages because of its simplicity and ease of use. This module is designed in such a way that even students who don't have a programming background can excel in it.

In this module, you will learn about

  • Basics of Python Programming
  • Data Types and Conditional Statements
  • OOP and Functional Programming
  • File Handling, Exceptions, and Data Analysis with Panda
  • Numerical Computing with Numpy, 
  • GUI development using tkinter and SQLite Database

In this course, you will be working on exciting challenges at the end of each topic and two full time projects, which will help you solidify your basics and apply them in real world situations.

4Machine Learning for Electrical Engineers using Python

Machine Learning is the study of computer algorithms and it is a subset of Artificial Intelligence. The purpose of ML is to create models based on training data to make predictions.

In this module, you will learn about

  • Basics of Data Science and Tools used.
  • Basics of Programming and Essential Python Libraries
  • Introduction to ML and Evaluation Metrics
  • Importing Data and Hands on Imported Data
  • Univariate &  Multivariate Linear Regression
  • Logistic Regression and K-nearest Neighbour
  • Principal Component Analysis
  • Decision Tree and Random Forest
  • K-Mean and Hierarchical Clustering
  • Neural Network

 

5Machine Learning & Artificial Intelligence for Mechanical, Civil & EE Engineers using Python

This module dwells deep into the concepts of Machine Learning Techniques. In this advanced module, you will be learning about

  • Basics of Probability and Statistics
  • Basics of ML & AI
  • Supervised Learning (Prediction, Classification)
  • Random Forest and Model Evaluation
  • Unsupervised Learning (K-Means, Hierarchical, Classification & PCA)

6Advanced Deep Learning

The last module of this course covers the basics and advanced concepts of Deep Learning. Deep Learning is a subset of Machine Learning in AI. Nowadays, Deep Learning is extensively used in Self Driving Cars, Natural Language Processing, Healthcare, Fraud Detection, Visual Recognition and Entertainment. Deep Learning is nothing but teaching computers to think using structures modelled on the human brain.

In this module, you will learn about

  • ANN - Artificial Neural Network (Feed Forward Neural Network)
  • Activation Functions in Neural Networks
  • Evaluation and Improvisation of Models
  • Optimizers
  • CNN & RNN and
  • Basics of NLP 


1. Math behind Machine Learning & Artificial Intelligence using Python Syllabus

1Basic concepts

  • Sets
  • Subsets
  • Power set
  • Venn Diagrams
  • Trigonometric functions
  • Straight lines
  • A.M, G.M and H.M
  • Concepts of Vectors

2Permutation & Combinations

  • Introduction & basics
  • Fundamental principle of counting
  • Permutations
  • Combinations

3Statistics - I

  • First business moment
  • Second business moment
  • Third business moment
  • Fourth business moment

4Probability

  • Introduction
  • Random experiments
  • Conditional probability
  • Joint probability

5Statistics – II

  • Z Scores
  • Confidence interval
  • Correlation
  • Covariance

6Probability - II

  • Introduction
  • Uniform Distribution
  • Normal Distribution
  • Binomial Distribution
  • Poisson Distribution

7Likelihood (for Logistic regression)

  • Introduction
  • Odds
  • Log odds
  • Maximum likelihood vs probability
  • Logistic regression

8Gradient descent (for Linear & Logistic regression)

  • Loss function
  • Cost function
  • Gradient descent for linear regression
  • Gradient descent for logistic regression

9Linear Algebra (for PCA)

  • Matrices
  • Types of matrices
  • Operation on matrices
  • Eigen values
  • Eigen vectors

10Derivatives (for Neural network)

  • Derivatives
  • Intuitive idea of derivatives
  • Increasing & decreasing function

11Backpropagation (for Deep learning)

  • Chain rule
  • Maxima & minima
  • Back propagation
  • Cost function for deep learning

12Python

  • Basics of Python
  • If else
  • For loop
  • Data types


Projects Overview

Logistic Regression

Highlights

To perform a logistic regression algorithm taught in the course with simple random sampling and stratified sampling and identify the differences in the result (Data will be given in the class)

Gradient Descent

Highlights

Perform gradient descent for ANN in Python

    • Choose any data set and based on that, come up with code for manually calculating the optimum weighted function and bias.
    • Compare the weights and bias with built in libraries

2. SQL for Data Science Syllabus

1Introduction

  • Introduction to Data Science 
  • Applications of Data Science 
  • Why SQL is required for Data Science? 
  • Database Management System (DBMS) 
  • Relational Database Management System (RDBMS) 
  • Basic terminology in RDBMS 
  • Data Constraints 
  • Entity Relationship Model 
  • What is SQL 
  • Categories of SQL Commands 
  • Hands on executing simple SQL statements on RDBMS tool

2Database Creation, Manipulation

  • Detailed SQL Data types 
  • Creating database
  • Create Table
  • Using Constraints
  • Insert Table
  • Altering Table structure
  • Dropping Database and Table
  • Delete, Update
  • Hands on importing sample database schema

3Database Selection

  • Select statement
  • Removing Duplicates
  • Use of Alias
  • Use of Where
  • Use of Wildcards
  • Limit clause
  • Arithmetic Operators
  • Mathematical Functions  
  • Hands on creating backups and restore for large databases

4Database Selection

  • Generating Strings
  • String Functions
  • Date Functions
  • Conversion Functions 

5Database Selection

  • Comparison Operators
  • Logical Operators
  • Order By
  • Group By
  • Aggregate Functions
  • Using aggregate functions with Group by clause
  • Union Operator
  • Sub-query

6Querying Multiple Tables

  • Need to Join Multiple Tables
  • Cartesian Product
  • Inner Join 
  • Left Join
  • Right Join
  • Self Join
  • Delete Join
  • Update Join
  • Hands on Joining more than two tables in sample database

7Data Exploration

  • What is Data Exploration?
  • Structure of Data
  • Understanding E-R Diagram
  • How to Use SQL for Data Exploration
  • Significance of 
    1. Joins
    2. Sub queries
    3. Inbuilt functions
  • Other important capabilities of SQL for data exploration
  • Hands On 
  • Working with NULL values
  • Making trends in Data
  • Identifying Outliers
  • Creating Data Summary

8Index, View, Transaction

  • Creating Index
  • Use of Index
  • Type of Index and Index Strategies
  • Views
  • Views as Weapon for Data Analysis
  • Multi user database
  • What is Transaction
  • Save points
  • Hands on working on Multi user database environment

9Querying with Conditions

  • Querying with Conditions 
  • Searched Case Expression 
  • Simple Case Expression 
  • Applications of Case Expression 
  • Common Error Codes 
  • Hands on working with Json type data

10Stored Procedures

  • Stored Procedures as friend for Data Analysis
  • Creating Stored Procedures
  • Removing Stored Procedures
  • Altering Stored Procedures
  • Conditional Statements
  • Loops
  • Hands on working with Cursors

11Integrating SQL with Excel

  • Hands on
  • Accessing MySQL data with MS Excel
  • Running SQL statements with Excel
  • Combining Excel and SQL statements for data representation

12Integrating SQL with Python

  • Hands on 
  • Working with Python
  • Accessing SQL data with Python
  • Running basic SQL statements with Python 
  • Running inbuilt python functions on SQL data


Projects Overview

Project 1

Highlights

  • Business model Customer to Customer (C2C) allows customers to do business with each other
  • Analyzing user’s database will lead to understand the business perspective
  • Behavior of the users can be traced in terms of business with exploration of the user’s database.

Project 2

Highlights

  • In the given dataset, alumni’s career choice will be analysed based on his course completion date
  • For data analysis, add-on for Microsoft excel is to be used for data summarization and visualization
  • Data computation can be performed with integration of python with SQL
  • SQL script should be ready with procedures and cursors using conditional statements and loops.

3. Core and Advanced Python Programming Syllabus

1Introduction to Python, Python Basics

• Discussion about Features and uses of Python, Program execution, Installation of IDE.
• Identifiers and keywords, types of comments, data types, Variables, Arithmetic operators, Assignment operators
• Input and print statements

2Strings, Decision control statements

• Definition of string, operations accessing string elements
• Relational operators, Logical operators, Conditional expressions, If, If..else, If..elif

3Repetition Statements, Console input-output

• Usage of while and for, break and continue, pass and else statements
• Formatted input and output

4Lists, Tuples, Sets, Dictionary

• Accessing list elements, basic list operations, types of lists
• Accessing tuple elements, types of tuples, tuple operation
• Accessing set elements, set operations and methods, Mathematical set operations, updating set operations
• Accessing dictionary elements, dictionary operations and methods, nested dictionary

5Functions and Recursion, Functional Programming and Lambda functions

• Defining a function, types of arguments
• Global and local variables
• Functions as arguments, Implementing Lambda functions
• Map, Reduce, Filter functions

6File Input-Output, Modules

• Read-write operations, with keyword, file opening modes, moving within a file
• Serialization, file and directory operations
• Importing a module, variations of import, third-party packages

7Classes and objects

  • Class variables and methods, Operator overloading
  • Reuse, Containership, Inheritance

8Exception handling, Iterators and generators

  • Iterables and iterators
  • Syntax errors and exceptions, try-except, else, finally blocks

9Data Analysis with Pandas

  • Installing Pandas
  • Loading CSV files, JSON files
  • Dataframes

10Numeric and Scientific Computing using Numpy

  • Introduction to Numpy
  • OpenCV
  • Images and Numpy Arrays

11Graphical User Interfaces with Tkinter

  • Introduction to Tkinter
  • Setting up a GUI with widgets
  • Connecting GUI widgets with Callback functions

12Interacting with Databases

  • Introduction to SQLite
  • Connecting and inserting data to SQLite via Python
  • Selecting, deleting and updating SQLite records


Projects Overview

Project 1

Highlights

This project uses the skills learnt from week 1 to week 6. The aim of this project is to create an English Dictionary app that returns definitions of English words. When the application is started, it displays a menu as follows:

Main Menu
1. Add a new word
2. Find the meaning
3. Update a word
4. Exit


Enter Choice:

If item 1 is selected, it should prompt for a word and then accept its meaning. This pair of word and its meaning should be stored in a file called ‘words.txt’. When item 2 is selected, it should prompt for a word, search that word in the file ‘words.txt’ and return the meaning if it is found. If the meaning is not found, it should display an appropriate message. Similarly, when choice 3 is entered, it should accept a word and update its meaning. In all the above three cases, the prompt should return to the main menu. The words and their meanings should be stored as a dictionary in words.txt, using the concept of serialization. If choice 4 is selected, a graceful exit should be performed.

Project 2

Highlights

In this project, a Library Book Management System will be implemented with the front end GUI developed using Tkinter and the backend database implemented in SQLite. It is a single window tkinter GUI application which will implement CRUD( create, read, update,delete) operations on a ‘books’ database, in SQLite. The window will contain text widgets to accept/ display book title, author, year of publication, ISBN (optional) and a large text area/ list box to display the records which match the search criteria. It should contain buttons to perform the following operations on the ‘books’ database:

1. Display all the records
2. Search a book
3. Add a book
4. Issue a book
5. Delete a book
6. Exit the application

When option 1 is selected, all the books present in the library should be displayed. Option 2 should be able to search the book based on any of the criteria like author name, book title, year or ISBN. Similarly, it should be able to add the entry when option 3 is selected and issue a book when option 4 is selected. The book details should be deleted when option 5 is selected. Selecting option 6 should provide a graceful exit from the app.


4. Machine Learning for Electrical Engineers using Python Syllabus

1Introduction to data science and programming languages (tools) for data science

Introduction about data science and big data and its importance and will introduce about various programming languages (tools) used for data science.

2Basics of programming

Explanation about variables, operators, data types, data structure, control structure in Python, Function file in Python

3Essential Python libraries

Include Numpy, Scipy, Pandas, Matplotlib, Seaborn, etc.

4Introduction to machine learning Cross- validation and bias variance tradeoff

This module will include basics of machine learning and its classification and include fitting of model with cross validation and bias variance trade off.

5Evaluation metrics

Evaluation metrics for model validation

6Importing data and hands on imported data

EDA/ correlation/ feature extraction/ hyper parameters

7Univariate and multivariate linear regression

This module will introduce univariate and multivariate linear regression and will explain how it can be implemented in Python

8Principal component analysis

Explanation about Eigen values and Eigen vectors and singular value decomposition and then PCA

9Logistic regression and k-nearest neighbor

Explanation and implementation of logistic regression and k-nearest neighbor in Python

10Decision tree and Random forest

Explanation and implementation of Decision tree and Random forest in Python

11K-mean and Hierarchical clustering

Clustering of data using clustering algorithm

12Neural network

Explanation of logistic regression with neural network mindsets


Projects Overview

Project 1

Highlights

 “EarlySalary” a stock forecasting company and has employed you as a Data Scientist. As a first job, the manager has provided you with stock market data and asked you to check the quality of data. There are two files “Stock_File_1.csv” and “Stock_File_2.txt”. Some details of the data shared with you are

a. The data set consists of six variables namely-date, Open, High, Low, Close, Volume

b. The stock market opens at 9:15 am and closes at 3:30 pm.

Each stock is defined by an opening price and closing price i.e. the price at which it opens and the price at which it closes. During operating regime, the stock prices touches maximum and minimum price. You have an access to tens years of monthly stock price data with the open, high, low, close and the volume that signifies the number of stocks traded. On some day there is no trading, the open, high, low, close remains constant and the volume is zero. Now, you go to your manager after data visualization and exploratory analysis and model building (do not build a model, assume you built it for sake), your manager says the model predictions are poor as the data is polluted (reported by manager that that instant). Now try to impress your boss by doing some data preprocessing. Assume that you fill missing values by mean of the data corresponding to each feature. Remenber: please merge the data before preprocessing. You can use pandas.concat (read) to merge your data

Project 2

Highlights

  •  “Sandman” is willing to do some manufacturing analytics for their manufacturing plant. Description: The datasets consists of 13 variables and 388 samples. The table below gives the description of the data
  • Sandman corporation continuously looks to improve its manufacturing quality. Sandman monitors quality of input materials (physical and chemical properties) on a daily basis. Also, it monitors quality of output material for every batch and output rejection rates are available on daily basis. Sandman wishes to develop a early warning system (EWS) to predict likely rejection for the new day, given the input quality for that day. They hired you to develop EWS. Put all the knowledge gathered in the course to achieve your objective.

5. Machine Learning & Artificial Intelligence for Mechanical, Civil & EE Engineers using Python syllabus

1Basics of Probability and Statistics

In this module, we will introduce the ideas of Statistics, machine learning and artificial intelligence. 

Basics of Probability

Basics of Statistics

What is ML & AI


2Basics of ML & AI

In this section, we start with supervised learning

Introduction to normal distribution & standard normal distribution

Introduction to business moments

Artificial Intelligence


3Supervised Learning - Prediction

In this section, we start with supervised learning

Introduction to supervised learning

What is linear regression

One hot encoding

Cost function and gradient descent

    

4Supervised Learning - Classification

In this section, we start with classification algorithm

Introduction to classification problems

What is logistic regression

Cost function and gradient descent


5Supervised Learning - Classification

In this module, we will introduce some more classification algorithms

Decision tree

Entropy

Information gain


6Random forest & Model Evaluation

In this section, we will assess the algorithms

Random Forest

Bootstrapping and majority rule

Evaluation of classifiers


7Supervised Learning - Classification

In this module, we will introduce SVM

Support Vector Machines

Mathematical intuition behind SVM 

How SVM is different from other classifiers


8Supervised Learning - Classification

In this section, we will introduce knn

K-Nearest Neighbour

Lazy Algorithm

Single layer Neural Network


9Unsupervised Learning - Kmeans

In this section, we will introduce clustering

What is clustering

Why clustering is important

Kmeans and elbow curve


10Unsupervised Learning - Hierarchical

In this section, we will introduce another type of clustering

Hierarchical Clustering

Dendrogram

Evaluation of clustering algorithms


11Unsupervised Learning - PCA

In this module, we will introduce some feature selection techniques

Feature Selection

Principal Component Analysis

Mathematical intuition behind PCA


12Supervised Learning - Classification

In this section, we will introduce neural network

Artificial Neural Network

Deep learning

Different activation functions

Understanding back propagation



Projects Overview

Highlights

Data cleaning on Automobile 1985 dataset and perform descriptive analytics

 

Project 2

Highlights

Implement Clustering and then predict the class of car from “Car dataset”

 


6. Advanced Deep Learning Syllabus

1Artificial Neural Network (Feed Forward Neural Network)

The introductory week will look into forward feed neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction forward-from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network. We will also look into -

  • Neural Network
  • Different architecture of Neural Network
  • Importance of Neural Network
  • Hyperparameters in Neural Network
  • Different types of Gradient descent methods

2Activation functions in neural networks

In the second week, we will look into activation functions. Activation functions are mathematical equations that are used to determine the output of a neural network. The function is attached to each neuron in the network, and is used to determine whether it should be activated or not, based on whether each neuron's input is relevant for the model's prediction. In activation functions, we will look into- 

  • Conic sections
  • Hyperbolic trigonometric functions
  • Sigmoid activation function
  • Tanhx activation function
  • Relu activation function
  • Softmax activation function

3Deep Learning

Deep learning is a branch of machine learning which is based on artificial neural networks. A neural network is a mimic of the human brain, so in short deep learning is also a mimic of human learning techniques. In deep learning, the software engineer doesnt program everything, instead we focus on developing our neural network. In this week, we will be looking into-

  • Terminologies in deep learning
  • Nomenclature
  • Order of vectorized forms
  • Forward propagation derivation with 1 layer
  • Back propagation derivation with 1 layer
  • Batch size, Iteration and Epoch

4Evaluation of models

The performance of a machine learning model can be characterized in terms of the bias and the variance of the model. A model with high bias makes strong assumptions about the form of the unknown underlying function that maps inputs to outputs in the dataset, such as linear regression. A model with high variance is highly dependent upon the specifics of the training dataset, such as unpruned decision trees. So, what are bias and variance?- Bias is the simplifying assumptions made by the model to make the target function easier to approximate and variance is the amount that the estimate of the target function will change given different training data. So, in this week we will look into different topics related to evaluation of models-

  • Underfitting
  • Overfitting
  • Lasso regularization
  • Ridge regularization
  • Elastic Net regularization

5Improvise the model

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance, bias, or improve predictions.

  • Sparse and convex functions
  • Bagging to avoid overfitting
  • Boosting to avoid underfitting
  • Stacking to avoid underfitting

6Optimizers

Regularization is a set of techniques that can prevent overfitting in neural networks and thus improve the accuracy of a Deep Learning model when facing completely new data from the problem domain. And Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. In this week we will look into the different types of regularisers and optimisers- 

  • Frobenius norm regularization
  • Data augmentation
  • Early stopping
  • Adam optimizer
  • Tensorflow 2.0

7CNN-1

In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. In this week we will look into more topics related to CNN such as-

  • Basics of CNN
  • Edge detection
  • Padding
  • Stride
  • Simple CNN
  • Difference between CNN & ANN

8CNN-2

This week is a continuation of the previous week’s topic. Here we will look into more topics related to CNN.

  • Pooling layers
  • Transfer learning
  • Examples on CNN architecture
  • Combination of different Neural network architecture
  • CNN in python

9RNN-1

A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

  • RNN Model
  • Different types of RNN
  • Gradients in RNN
  • Back propagation
  • Difference between RNN & ANN

10RNN-2

This week is a continuation of the previous week’s topic. Here we will look into more topics related to RNN.

  • Gated Recurrent Unit (RNN)
  • Long short term memory (LSTM)
  • Bidirectional RNN
  • RNN Implementation in Python

11Basics of NLP

Natural Language Processing(NLP) refers to computer systems designed to understand human language. Human language, like English or Hindi consists of words and sentences, and NLP attempts to extract information from these sentences. Word embedding is any of a set of language modeling and feature learning techniques in natural language processing where words or phrases from the vocabulary are mapped to vectors of real numbers.

  • Stop words
  • Stemming
  • Lemmatization
  • Word2vec
  • Implementation of word2vec in python

12End-to-End ML Project steps

In this week, we will be performing an end-to-end ML/DL project for machine failure.

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics


Projects Overview

Project 1

Highlights

Perform 3 Gradient Descent on the given dataset using python

  1. Batch Gradient Descent with all data points (Plot loss v/s iteration)
  2. Minibatch Gradient descent with batch size=100 (Plot loss v/s epoch)
  3. Stochastic Gradient descent with batch size=1 (Plot loss v/s epoch)

Project 2

Highlights

Write the steps of gradient descent for logistic regression with output having 3 classes and 2 input features, n samples on paper/word document

Project 3

Highlights

Perform gradient descent for the small dataset which has 3 input features, 1 binary output. Consider the hidden layer with 4 units with activation function as sigmoid on python/paper

Project 4

Highlights

Perform Linear Regression & check for bias/variance and try avoiding it

Project 5

Highlights

Identify the working principle of all 5 Gradient boosting method & list down its advantage & disadvantage

Project 6

Highlights

Prediction of machine failure for the given dataset using ANN (Hyperparameters is completely dependent on individuals to come up with the best model)

Project 7

Highlights

Identify the formula behind output dimension in terms of both padding and stride

Project 8

Highlights

Identify the dataset from opensource which has binary classification on images and perform CNN (or) Perform CNN on the given dataset which has 17 classes on different vehicles

Project 9

Highlights

Explain vanishing gradient problem in RNN

Project 10

Highlights

Perform RNN/LSTM/GRU/Bi-directional on spam filtering dataset

Project 11

Highlights

Perform either Machine Learning or Deep Learning algorithm on Credit card fraud detection dataset (Its completely up to student which algorithm/strategy they want to apply on this dataset)


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  • Top 5% of the class will get a merit certificate
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FAQ

1Who are the instructors and what is the learning process?

The instructors who have created the courses would have an average of 7 years experience in the field of Data science and the learning process would be an introduction to the Maths & Statistics involved behind Machine Learning & AI hypothesis testing, data mining, clustering, decision trees, linear and logistic regression, programming in R,SQL, then followed with  data wrangling, data visualization, regression models and how to use the libraries. It will conclude with how you can view, visualize and work with the data you have to make data-driven decisions. By completing this you will have gained skills and help you prepare for the role of Data scientist.

2Are there any prerequisites for this course?

Programming in Python (Fundamentals)

3What kind of support I can expect? What if I have doubts?

Your doubts will be cleared by our in-house Technical support engineers.

4How is this different from what I learnt in college?

In your under graduation, you would have learnt the Mathematical concepts to a certain extent and a few related theoretical concepts on Data Science, but here you would be applying the concepts being covered in our courses in real-time & industry relevant projects.(Data sets would be similar to what is being used in the industry)


5What advantages will I gain by taking this course?

This course will cover topics and projects to give you extensive knowledge and gain hands-on experiences of what you can look forward to doing in the industry as a Data Analyst/Scientist.


6Will the software be provided?

Yes, Software will be provided.



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