PG/Masters Program in Data Science and AI

• Concepts & Definitions 
• Myth with IoT
• Business with IoT
• Carrier in IoT
• IoT Applications
• IoT system overview 
• Node, Gateway, Clouds 
• Why IoT is essential
• Machine learning
• Artificial Intelligence

• IoT Network Architecture
• IoT Device Architecture
• IoT Device Architecture
• Publish-Subscribe architecture

• Sensors – Classification & selection criteria based on the nature, frequency and amplitude of the signal
• Embedded Development Boards – Arduino, Raspberry Pi, Intel Galileo, ESP8266

• Wired Communication Protocols
• Wireless Communication Protocols 
• Application Protocols – MQTT, CoAP, HTTP, AMQP
• Transport layer protocols – TCP vs UDP
• IP- IPv4 vs IPv6

• Concept & Architecture of Cloud
• Public cloud vs Private cloud
• Different Services in cloud (IAAS / PAAS / SAAS)
• Importance of Cloud Computing in IOT
• Leveraging different Cloud platforms.

• Interfacing peripherals & Programming GPIOs – Input/output peripherals, Sensor modules
• Design Considerations – Cost, Performance & Power Consumption tradeoffs

• Embedded C
• Python
• Arduino

• Setting up board
• Booting up Raspberry Pi
• Running python on Raspberry Pi, GPIO programming
• Interfacing sensors and LED (Input and output devices)
• Making a few projects
• Sending data to cloud 2 using Raspberry Pi board
• Sending data to cloud 3 using Raspberry Pi board
• Making raspberry Pi web server
• Making raspberry PI TCP client and server
• Making raspberry Pi UDP client and server

• A cloud-based temperature monitoring system using Arduino and Node MCU
• Esp8266 WIFI controlled Home automation
• Obstacle detection using IR sensor and Arduino
• Remote controlling with Node MCU
• Temperature monitoring using a Raspberry Pi as local server
• Raspberry Pi controlling Esp8266 using MQTT
• weather monitoring system using Raspberry Pi and Microsoft Azure cloud

• Existing Product in Market
• Barrier in IoT

• Databases
• Introduction to DBMS
• Popular DBMS Software
• Concepts of RDBMS
  • Tables
  • Tuples
  • Attributes
• Normalization
  • First Normal Form
  • Second Normal Form
  • Third Normal Form
• NoSQL Databases
  • Types of NOSQL
• Comparison

• Types of SQL Commands
• Data Definition Language
  • Create, Drop, Truncate, Alter and Rename Objects
• Data Query Language
  • Select Statements
• Data Manipulation Language
• DCL and TCL
  • Grant, Revoke and transaction statements
• SQL Data Types
  • Numeric, Date and Time, LOB Types
• DML Commands
  • Insert, Update and Delete Statements
• DDL Commands
  • Create and Drop Databases

• Types of Constraints
  • Relational Integrity Constraints
  • Key Constraints
  • Domain Constraints
  • Referential Integrity
• Types Of Constraints
  • Primary and Foreign Keys
  • Application of Indexes
  • Checking Constraints
• Alter Tables

• SQL Transactions
  • Examples
• ACID Properties
• TCL Statements
  • Start, Commit and Rollback Statements
• Auto Commit
• Save Points
  • Identifier
  • Rollback and Release

• Tables
  • Creating, Altering and dropping tables
• Sequences
  • Auto Increments
  • Re-Sequencing
• Views
  • Advantages
  • Creating and Dropping Views
• Indexes
  • Types of Indexes
  • B-Tree and Hash Indexes
  • Creating and dropping Indexes

• Stored Objects
  • Types of Stored Objects
• Stored Procedures
  • Create, call and drop stored procedures
  • Using Variables
  • Handling Exceptions
  • Named Errors and Resignals
• Programming
  • If-then-Else and Case Statements
  • Loops
  • Repeat and Leave Statements
  • Cursors
• Operators and Functions
• Joining Tables
  • Inner Join, Left Join, Right join
• Advantages of Procedures

• Triggers
  • Database Triggers
  • Data Definition Language (DDL) Triggers
  • Data Manipulation Language (DML) Triggers
  • CLR Triggers
  • Logon Triggers
  • Triggers v/s Stored Procedures
• Accessing Database from R
  • Install R Packages
  • Configuration Information
• Python Database Access
  • Databases Supported
  • Libraries
  • Read Operations
  • Insert, Update and Delete
  • Performing Transactions
  • Handling Errors

• Introduction to What is DataBase
• Difference between SQL and NOSQL DB
• How to Install MYSQL and Workbench
• Connecting to DB
• Creating to DB
• What are the Languages inside SQL How to Create Tables inside DB and Inserting the Records
• Select statement and using Queries for seeing your data
• Joining 2 tables
• Where clause usage
• Indexes and views
• Different operations in SQL
• How to Connect to your applications from MYSQL includes R and Python

• Introduction to R
• Data Types in R

How To Install R & R Studio

• Variable in R
• R-Overview
  • Vector
  • Matrix
  • Array
  • List
  • Data-Frame
• Operators in R
  • arithmetic
  • Relational
  • Logical
  • Assignment
  • Miscellaneous
• Conditional Statement
  • Decision Making<
    • IF Statement
    • IF-Else Statement
    • Nested IF-Else Statement
    • Switch Statement
  • Loops
    • While Loop
    • Repeat Loop
    • For Loop
  • Strings
  • Functions
    • User-defined Function
    • Calling a Function
    • Calling a Function without an Argument
    • Calling a Function with an Argument

• Box Plots
• Bar Charts
• Histogram
• Pareto Chart
• Pie Chart
• Line Chart
• Scatterplot

• Read CSV Files
• Read Excel Files
• Read SAS Files
• Read STATA Files
• Read SPSS Files
• Read JSON Files
• Read Text Files

• DpLyr
• Hmisc or mise
• Ggplot2
• Caret
• Data Table

How to Integrate R and SQL

How to Get Data From SQL to R

• Python Introduction - Programing Cycle of Python
• Python IDE

• Variables
• Data type
• Number
• string
• List
• Tuple
• Dictionary

• Operator -Arithmatic
• comparison
• Assignment
• Logical
• Bitwise operator

• While loop, if loop and nested loop
• Number type conversion - int(), long(). Float ()
• Mathametical functions , Random function , Trigonometric function

• Strings- Escape char, String special Operator , String formatting Operator
• Build in string methods - center(), count()decode(), encode()

• Python List - Accessing values in list, Delete list elements , Indexing slicing & Matrices
• Built in Function - cmp(), len(), min(), max()

• Tuples - Accessing values in Tuples, Delete Tuples elements , Indexing slicing & Matrices
• Built in tuples functions - cmp(), len ()

• Dictionary - Accessing values from dictionary, Deleting and updating elements in Dict.
• Properties of Dist. , Built in Dist functions & Methods.
• Date & time -Time Tuple , calendor module and time module

• Function - Define function , Calling function
• pass by refernece as value , Function arguments , Anonymous functions , return statements
• Scope of variables - local & global

• Import statemnts , Locating modules - current directory , Pythonpath
• Dir() function , global and location functions and reload () functions .
• Packages in Python

• Files in Python- Reading keyboard input , input function
• Opening and closing files . Syntax and list of modes
• Files object attribute- open , close . Reading and writing files , file Position.
• Renaming and deleting files

• mkdir methid, chdir () method , getcwd method , rm dir

• Exception handling - List of exceptions - Try and exception
• Try- finally clause and user defined exceptions

• OOP concepts , class , objects , Inheritance
• Overriding methods like _init_, Overloading operators , Data hiding

• match function , search function , matching vs searching
• Regular exp modifiers and patterns

• What is CGI .,Architecture of CGI , Web server support get and post () methods.

• Introduction
• Tkinter programming
• Tkinter widgets

• Data base connectivity
• Methods- MySQL , oracle , how to install MYSQL , DB connection
• create , insert , update and delete operation , Handling errors

• Into Mult Threading
• Threading module
• creating thread
• Synchronizing threads
• Multithreaded Priority Queue

• Introduction to Django framwork
• overview
• enviorment
• Apps life cycle
• creating views
• Application

• Numpy,Pandas,Matplotlib

Assignments

Projects

• Recap of Demo
• Introduction to Types of Analytics
• Project life cycle

Description: Learn about High-level overview of Data Science project management methodology, Statistical Analysis using examples, understand Statistics and Statistics 101. Also, learn about exploratory data analysis, data cleansing, data preparation, feature engineering.

Topics

• High-Level overview of Data Science / Machine Learning project management methodology
• Videos for Data Collection - Surveys  and Design of Experiments will be provided
• The various Data Types namely continuous, discrete, categorical, count, qualitative, quantitative and its identification and application. Further classification of data in terms of Nominal, Ordinal, Interval and Ratio types
• Random Variable and its definition
• Probability and Probability Distribution – Continuous probability distribution / Probability density function and Discrete probability distribution / Probability mass function

Description: Continue with the discussion on understanding Statistics, the various Moments of business decision and other Basic Statistics Concepts. Also, learn about some graphical techniques in Analytics.

Topics

• Balanced vs Imbalanced datasets
• Various sampling techniques for handling balanced vs imbalanced datasets
• Videos for handling imbalanced data will be provided
• What is Sampling Funnel, its application and its components
  • Population
  • Sampling frame
  • Simple random sampling
  • Sample
• Measure of central tendency
  • Mean / Average
  • Median
  • Mode
• Measure of Dispersion
  • Variance
  • Standard Deviation
  • Range
• Expected value of probability distribution

Description: Learn about the other moments of business decision as part of Statistical Analysis. Learn more about Visual data representation and graphical techniques. Learn about Python, R programming with respect to Data Science and Machine Learning. Understand how to work with different Python IDE and Python programming examples.

Topics

• Measure of Skewness
• Measure of Kurtosis
• Various graphical techniques to understand data
  • Bar plot
  • Histogram
  • Box plot
  • Scatter plot
• Introduction to R and RStudio  
• Installation of Python IDE
• Anaconda and Spyder
• Working with Python and R with some basic commands

Description: Learn about Normal Distribution and Standard Normal Distribution. Rules and Principles of Normal distribution. And how to check for normality by QQ normal distribution Plot.

Topics

• Normal Distribution
• Standard Normal Distribution / Z distribution
• Z scores and Z table
• QQ Plot / Quantile-Quantile plot

Description: Under this last topic on Basics of statistics, learn some higher statistical concepts and gain understanding on interval estimates.

Topics

• Sampling Variation
• Central Limit Theorem
• Sample size calculator
• T-distribution / Student's-t distribution
• Confidence interval
  • Population parameter - Standard deviation known
  • Population parameter - Standard deviation unknown

Introduction to R and Python basic stats

Description: Get introduced to Hypothesis testing, various Hypothesis testing Statistics, understand what is Null Hypothesis, Alternative hypothesis and types of hypothesis testing.

Topics

• Parametric vs Non-parametric tests
• Formulating a Hypothesis
• Choosing Null and Alternative hypothesis
• Type I and Type II errors
• Comparative study of sample proportions using Hypothesis testing
• 2 sample t test

Description: Learn about the various types of tests in Hypothesis testing. Get introduced to the prerequisites and conditions needed to proceed with a Hypothesis test. Understand the interpretation of the results of a Hypothesis testing and probabilities of Alpha error.

Topics

• 1 sample t test
• 1 sample z test
• ANOVA
• 2 Proportion test
• Chi-Square test
• Non-Parametric test

Description: Continuing the discussion on Hypothesis testing, learn more about non-parametric tests. Perform different tests and interpret the results.

Topics

• Non-Parametric test continued
• Hypothesis testing using Python and R

• Scatter Diagram
• Correlation Analysis
• Principles of Regression
• Introduction to Simple Linear Regression
• R shiny and Python Flask
  • Introduction to R shiny and Python Flask (deployment)
• Multiple Linear Regression

Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.

Topics

• Scatter diagram
  • Correlation Analysis
  • Correlation coefficient
• Ordinary least squares
• Principles of regression
• Splitting the data into training, validation and testing datasets
• Understanding Overfitting (Variance) vs Underfitting (Bias)
• Generalization error and Regularization techniques
• Introduction to Simple Linear Regression
• Heteroscedasticity / Equal Variance

Description: In the second part of the tutorial, you will learn about the Models and Assumptions for building Linear Regression Models, build Multiple Linear Regression Models and evaluate the results of the Linear Regression Analysis.     

Topics

• LINE assumption
  • Collinearity (Variance Inflation Factor)
  • Linearity
  • Normality
• Multiple Linear Regression
• Model Quality metrics
• Deletion diagnostics

Description: Learn to analyse Attribute Data, understand the principles of Logistic Regression, Logit Model. Learn about Regression Statistics and Logistic Regression Analysis.

Topics

• Principles of Logistic Regression
• Types of Logistic Regression
• Assumption and Steps in Logistic Regression
• Analysis of Simple Logistic Regression result

Description: Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures and its interpretation. Know what is a confusion matrix and its elements. Get introduced to “Cut off value” estimation using ROC curve. Work with gain chart and lift chart.     

Topics

• Multiple Logistic Regression
• Confusion matrix
  • False Positive, False Negative
  • True Positive, True Negative
  • Sensitivity, Recall, Specificity, F1
• Receiver operating characteristics curve (ROC curve)
• Lift charts and Gain charts 

• Lasso and Ridge Regressions

Description: Get introduced to Multinomial regression, or otherwise known as multinomial logistic regression, learn about multinomial logit models and multinomial logistic regression examples.

Topics

• Logit and Log Likelihood
• Category Baselining
• Modeling Nominal categorical data
• Additional videos are provided on Lasso / Ridge regression for identifying the most significant variables

Description: As part of Data Mining Unsupervised get introduced to various clustering algorithms, learn about Hierarchical clustering, K means clustering using clustering examples and know what clustering machine learning is all about.

Topics

Hierarchical

• Supervised vs Unsupervised learning
• Data Mining Process
• Measure of distance
  • Numeric - Euclidean, Manhattan, Mahalanobis
  • Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
  • Mixed - Gower’s General Dissimilarity Coefficient
• Types of Linkages
  • Single Linkage / Nearest Neighbor
  • Complete Linkage / Farest Neighbor
  • Average Linkage
  • Centroid Linkage
• Hierarchical Clustering / Agglomerative Clustering

Description: In this continuation lecture learn about K means Clustering, Clustering ratio and various clustering metrics. Get introduced to methods of making optimum clusters.

Topics

• Non-clustering
  • K-Means Clustering
  • Measurement metrics of clustering - Within Sum of Squares, Between Sum of Squares, Total Sum of Squares
  • Choosing the ideal K value using Scree plot / Elbow Curve
• Additional videos are provided to understand K-Medians, K-Medoids, K-Modes, Clustering Large Applications (CLARA), Partitioning Around Medoids (PAM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS)

Description: Learn to apply data reduction in data mining using dimensionality reduction techniques. Gain knowledge about the advantages of dimensionality reduction using PCA and SVD.

Topics

• Why dimension reduction
• Advantages of PCA
• Calculation of PCA weights
• 2D Visualization using Principal components
• Basics of Matrix algebra
• SVD – Decomposition of matrix data

Description: Under data mining unsupervised techniques, learn about Network Analytics and the measures used. Get introduced to Network Analysis tools like NodeXL.

Topics

• Definition of a network (the LinkedIn analogy)
• Measure of Node strength in a Network
  • Degree centrality
  • Closeness centrality
  • Eigenvector centrality
  • Adjacency matrix
  • Betweenness centrality
  • Cluster coefficient
• Introduction to Google Page Ranking

Description: Learn one of the most important topic Association rules in data mining. Understand how the Apriori algorithm works, and the association rule mining algorithm.

Topics

• What is Market Basket / Affinity Analysis
• Measure of association
  • Support
  • Confidence
  • Lift Ratio
• Apriori Algorithm
• Sequential Pattern Mining

Description: Learn how online recommendations are made. Get insights about online Recommender System, Content-Based Recommender Systems, Content-Based Filtering and various recommendation engine algorithms. Get to know about people to people collaborative filtering and Item to item collaborative filtering.

Topics

• User-based collaborative filtering
• Measure of distance / similarity between users
• Driver for recommendation
• Computation reduction techniques
• Search based methods / Item to item collaborative filtering
• SVD in recommendation
• Vulnerability of recommender systems

Description: Text mining or Text data mining is one of the wide spectrum of tools for analyzing unstructured data. As a part of this course, learn about Text analytics, the various text mining techniques, its application, text mining algorithms and sentiment analysis.

Topics

• Sources of data
• Bag of words
• Pre-processing, corpus Document-Term Matrix (DTM) and TDM
• Word Clouds
• Corpus level word clouds
  • Sentiment Analysis
  • Positive Word clouds
  • Negative word clouds
  • Unigram, Bigram, Trigram
• Semantic network
• Clustering

Description: Learn how to extract data from Social Media, download user reviews from E-commerce and Travel websites. Generate various visualizations using the downloaded data.     

Topics

• Extract Tweets from Twitter
• Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor

Description: Learn how to perform text analytics using Python and work with various libraries that aid in data extraction, text mining, sentiment analysis and  

Topics

• Install Libraries from Shell
• Extraction and text analytics in Python

Description: Natural language processing applications are in great demand now and various natural language processing projects are being taken up. As part of this tutorial, learn about Natural language and ‘Natural language understanding’.

Topics

• LDA
• Topic Modeling 
• Sentiment Extraction
• Lexicons and Emotion Mining

Description: Learn about Machine Learning modeling using KNN, the K nearest Neighbor algorithm using KNN algorithm examples. The KNN classifier is one of the most popular classifier algorithms.

Topics

• Deciding the K value
• Building a KNN model by splitting the data
• Understanding the various generalization and regulation techniques to avoid overfitting and underfitting

Description: Under the Naive Bayes classifier tutorial, learn how the classification modeling is done using Bayesian classification, understand the same using Naive Bayes example. Learn about Naive Bayes through the example of text mining.

Topics

• Probability – Recap    
• Bayes Rule
• Naive Bayes Classifier
• Text Classification using Naive Bayes

Description: Decision Tree and Random Forest are one of the most powerful classifier algorithms today. Under this tutorial, learn about Decision Tree Analysis, Decision Tree examples and Random Forest algorithms.

Topics

• Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
• Greedy algorithm
• Measure of Entropy
• Attribute selection using Information Gain
• Ensemble techniques
• Decision Tree C5.0 and understanding various arguments
• Random Forest and understanding various arguments

Description: Bagging and Boosting is an ensemble technique which is a part of the random forest algorithm. Learn about Bagging and Boosting examples under this tutorial.

Topics

• Boosting / Bootstrap Aggregating
• AdaBoost / Adaptive Boosting
• Stacking
• Gradient Boosting
• Extreme Gradient Boosting (XGB)

Description: Artificial Neural Network and Support Vector Machines are the two powerful Deep learning algorithms. Get introduced to Neural Net, Convolutional Neural Network, Recurrent Neural Network. Learn how to work with Support Vector Machine, SVM classifiers and SVM regression.

Topics

• Artificial Neural Network
• Biological Neuron vs Artificial Neuron
• ANN structure
• Activation function
• Network Topology
• Support Vector Machines
• Classification Hyperplanes
• Best fit “boundary”
• Kernel Trick

• Concept with a business case

Description: Forecasting or Time Series Analysis is an important component in analytics. Here, get to know the various forecasting methods, forecasting techniques and business forecasting techniques. Get introduced to the time series components and the various time series analysis using time series examples.

Topics

• Introduction to time series data
• Steps of forecasting
• Components of time series data
• Scatter plot and Time Plot
• Lag Plot
• ACF - Auto-Correlation Function / Correlogram
• Visualization principles
• Naive forecast methods
• Errors in forecast and its metrics
• Model Based approaches
  • Linear Model
  • Exponential Model
  • Quadratic Model
  • Additive Seasonality
  • Multiplicative Seasonality
• Model-Based approaches
• AR (Auto-Regressive) model for errors
• Random walk
• ARMA (Auto-Regressive Moving Average), Order p and q
• ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
• Data-driven approach to forecasting
• Smoothing techniques
  • Moving Average
  • Exponential Smoothing
  • Holts / Double Exponential Smoothing
  • Winters / HoltWinters
• De-seasoning and de-trending
• Econometric Models
• Forecasting Best Practices
• Forecasting using Python
• Forecasting using R

• Basic Statistics
  • Data types Identification and probability
  • Expected values, Measures of central tendencies
  • Skewness and Kurtosis & Boxplot
  • Practice Mean, Median, Variance, Standard Deviation and Graphical representations in R
  • Creating Python Objects
  • Practice Mean, Median, Variance, Standard Deviation and Graphical representations in Python
  • Confidence intervals and distributions
• Hypothesis Testing
  • Buyer ratio
  • Customer Order Form
  • Cutlets
  • Pantaloons
  • Lab TAT
• Linear regression
  • Prediction of weight based on Calories consumed
  • Delivery Time period Vs Sorting time
  • Employee Churn rate Vs Salary
  • Salary Prediction
• R shiny and Flask
  • Practice R shiny and Python Flask for Linear Regression assignments
• Multiple Linear Regression
  • 50 startups case study
  • Computer data Case study
  • Toyota Corolla
• Logistic Regression
  • Term deposit case study
  • Elections results Case study
• Multinomial Regression
  • Student Program Case study
• Hierarchical Clustering
  • Crime data
  • Eastwest Airlines
• K means Clustering
  • Insurance policy
  • Crime data
• PCA
  • Dimension Reduction for Wine data
• Network Analytics
  • Node Properties practice in R
• Association Rules
  • Association Rules for Book store
  • Association Rules for Mobile store
  • Association Rules for Retail Transactions
• Recommendation Engine
  • Recommend Jokes for subscribers
• Text mining, Web Extraction
  • Extraction of tweets from twitter
  • Reviews from ecommerce websites
• Text mining
  • Sentiment Analysis on extracted data
• NLP
  • Emotion mining by extracting a speech or novel from web
• Naive Bayes
  • Spam and Ham classifications
• KNN Classifier
  • Types of Glass
  • Classification of Animals
• Decision Tree and Random Forest
  • Fraud Check
  • Sales prediction of an Organization
• XGB and GLM
  • Social Networks Ads
• Lasso and Ridge Regression
  • Practice Lasso and Ridge with multiple Linear Assignments
• ANN
  • Forest Fires case study
• SVM
  • Classification of Alphabets
• Survival analysis
  • Prediction of Patient survival probability
• Forecasting model based
  • Airlines Forecasting
  • Forecasting of sales for a soft drinks case study
• Forecasting
  • Forecasting of Bike shares
  • Forecasting of Solar power consumption

• Industry : Aviation

  Predicting the flight delays

  • How to determine which flights would be delayed and by how long?


• Industry : Manufacturing

  Predict impurity in ore

  • The main goal is to use this data to predict how much impurity is in the ore concentrate As this impurity is measured every hour if we can predict how much silica (impurity) is in the ore concentrate, we can help the engineers giving them early information to take actions


• Industry : Oil and Gas

  Predicting the oil price

  • Oil production and prices data are for 1932-2014(2014 data are incomplete );gas production and prices are for 1955-2014 export and net export data are for 1986-2013


• Industry : Automotive

  Electric Motor Temperature

  • Predict the temperature of rotor and stator of E-Motor


• Industry : Daily Analysis of a product

  "Daily" Twitter Data Analysis for a Product

  • Sentiment Emotion mining of twitter data of new product


• Industry : E commerce

  Natural Language Processing

  • Top 5 relevant answers to be retrived based on input question

• Resume Preparation
• Interview Support

Basic Concept

• Train,Test & Validation Distribution
• ML Strategy
• Computation Graph
• Evaluation Metric
• Human Level Performance

Supervised

• Linear Regression
• Logistic Regression
• Gradient Descent
• Decision Tree
• Random Forest
• Bagging & Boosting
• KNN

Unsupervised

• K-Means
• Hierarichal Clustering

Python

• Basic Programming
• NLP Libraries
• OpenCV

Basic Statistics

• Sampling & Sampling Statistics
• Hypothesis Testing

Calculus

• Derivatives
• Optimization

Linear Algebra

• Function
• Scalar-Vector-Matrix
• Vector Operation

Probability

• Space
• Probability
• Distribution

Introduction

• Intro
• Deep Learning Importance [Strength & Limitation]
• SP | MLP

Feed Forward & Backward Propagation

• Neural Network Overview 
• Neural Network Representation
• Activation Function
• Loss Function
• Importance of Non-linear Activation Function
• Gradient Descent for Neural Network

Practical Aspect

• Train, Test & Validation Set
• Vanishing & Exploding Gradient
• Dropout
• Regularization

Optimization

• Bias Correction
• RMS Prop
• Adam,Ada,AdaBoost
• Learning Rate
• Tuning 
• Softmax

Environment

• Scikit Learn
• NLTK
• Spacy & Gensim
• OpenCV
• Tensorflow
• Keras

Text Processing

• Representation
• Data Cleaning
• Data Preprocessing
• Similarity

Image Processing

• Image
• Image Transformation
• Filters 
• Noise Removal
• Correlation & Convolution
• Edge Detection
• Non Maximum Suppression & Hysteresis
• Fourier Domain
• Video Processing

Speech Data Analytics

Feature Extraction

• Image Feature
• Descriptors

Object Detection

• Detection  & Classification

CNN

• Computer Vision
• Padding
• Convolution
• Pooling
• Why Convolution

Deep Convolution Model

• Case Studies
• Classic Networks
• Inception
• Open Source Implementation
• Transfer Learning

Detection Algorithm

• Object Localization
• Landmark Detection
• Object Detection
• Bounding Box Prediction
• Yolo

Face Recognition

• What is Face Recognition
• One Shot Learning
• Siamese Network
• Triplet Loss
• Face Verification
• Neural Style Transfer
• Deep Conv Net Learning

• Why Sequence Model
• RNN Model
• Backpropagation through time
• Different Type of RNNs
• GRU
• LSTM
• Bidirectional LSTM
• Deep RNN
• Word Embedding
• Debiasing
• Negative Sampling
• Elmo & Bert
• Beam Search
• Attention Model

• Autoencoders & Decoders
• Adversarial Network
• Active Learning

• Q Learning
• Exploration & Exploitation

Introduction to Machine Learning

• Business Case evaluation
• Data requirements and collection
• Evaluation metrics

Machine Learning

• Profit of 50_startups data prediction
• Extra marital affair prediction
• Fraud data analytics
• Fabric sales analysis
• Classification of animals data
• Crime data analysis using clustering method and airlines data to obtain optimum number of clusters.

Python Programming

• Resource Information Analysis
• Text Cleaning of Customer reviews using NLP
• Image Manipulation (Loading, Rotation etc.)

Mathematics Foundation

• Sampling & Sampling Statistics
• Hypothesis Testing
• Calculus Problems
• Linear Algebra Problems
• Probability Problems

Intro to Neural Network & Deep Learning

Parameter & Hyperparameter

• Risk Evaluation
• Prediction of claim amount
• Emotor temp prediction
• User Behavioural Pattern

(2 ANN assignments+ 2 Parameter and hyperparameters)

Data Processing

• User review data load and familiriaty with data and environment
• E commerce Product Similarity
• Sentiment classification of movie reviews
• Emotion Mining of user reviews"
• Vehicle edge detection
• Cleaning of hand-written digits data
• Image data Augumentation
• Facial feature detection
• Image data wrangling for classification
• Video Analysis of a short film
• Speech data Analysis w.r.t emotion

CNN

• Ecommerce product image classification
• Disease prediction based on images

(2 CNN algorithms)

• Vehicle identification(Object Detection)
• Animal Classification(Object Classification)
• Spatial Image classification (Image segmentation)
• Face detection
• Face recognition (Attendance using facial recognition)

RNN

• Next word prediction (Vanilla RNN)
• Twitter data analysis using Named Entity Recognition(NER)
• Retail data - Word2vec
• NER and Forecasting of Oil price prediction
• Auto text composer (NER language model)
• Auto text composer (NER language model)
• Q and A Chatbot
• Real life voice Recognition

Generative

• Machine Translation
• New Image generation based on existing images

Reinforcement Learning

• Game Intelligence

1.Chatbot project

• Build end to end chatbot right from data storage schema to final output for a domain

2.Emotion Analytics

• Identifying and analyzing the full spectrum of human emotions including mood, attitude and emotional personality.

3.Object Detection

• Detection of objects in images

4.Face detection from CC camera feed

• Analysis of video feed from CC cameras

• Why Visualization came into Picture?
• Importance of Visualizing Data
• Poor Visualizations Vs. Perfect Visualizations
• Principles of Visualizations
• Tufte’s Graphical Integrity Rule
• Tufte’s Principles for Analytical Design
• Visual Rhetoric
• Goal of Data Visualization

• Introduction to Tableau
• What is Tableau? Different Products and their functioning
• Architecture Of Tableau
• Pivot Tables
• Split Tables
• Hiding
• Rename and Aliases
• Data Interpretation

• Understanding about Data Types and Visual Cues

• Text Tables, Highlight Tables, Heat Map
• Pie Chart, Tree Chart
• Bar Charts, Circle Charts

• Time Series Charts
• Time Series Hands-On
• Dual Lines
• Dual Combination

• Bullet Chart
• Scatter Plot
• Introduction to Correlation Analysis
• Introduction to Regression Analysis
• Trendlines
• Histograms
• Bin Sizes in Tableau
• Box Plot
• Pareto Chart
• Donut Chart, Word Cloud
• Forecasting ( Predictive Analysis)

• Types of Maps in Tableau
• Polygon Maps
• Connecting with WMS Server
• Custom Geo coding
• Data Layers
• Radial & Lasso Selection

• How to get Background Image and highlight the data on it
• Creating Data Extracts
• Filters and their working at different levels 
• Usage of Filters on at Extract and Data Source level
• Worksheet level filters
• Context, Dimension Measures Filter

• Joins
• Unions
• Data Blending
• Cross Database Joins
• Sets
• Groups
• Parameters

• Logical Functions
• Case-If Function
• ZN Function
• Else-If Function
• Ad-Hoc Calculations
• Quick Table Calculations
• Level of Detail (LoD)
• Fixed LoD
• Include LoD
• Exclude LoD

• Dashboards
• Actions at Sheet level and Dashboard level
• Story

• Publishing our Workbooks in Tableau Server
• Publishing dataset on to Tableau Server
• Setting Permissions on Tableau Server

• What is R?
• How to integrate Tableau with R?
• Tableau Prep

• Introduction to Cloud Computing
• Difference between On Premise and Cloud
• Types of Service Models
• Advantages of Cloud Computing
• Azure Global Infrastructure
• Creation of Free tire account inside Azure
• Sample Instance Creating Both UNIX and Windows and connecting them on cloud
• Storage options and Creating Extra Storage and attaching to the VMs
• Blob Storage
• Creating DB instance
• Creating Custom VN
• Brief introduction to Machine Learning Services on Cloud and more

• Introduction to Big Data
• Challenges in Big Data and Workarounds
• Introduction to Hadoop and its Components
• Hadoop components and Hands-on
• Understand the MapReduce (Distributed Computation Framework) and its Drawback
• Introduction to Spark
• Spark Components
• Spark MLlib and Hands-on (one ML model in spark)

An overview of the screen, navigation and basic spreadsheet concepts

• Customizing the Ribbon
• Worksheets
• Format Cells
• Various selection techniques
• Shortcuts Keys
• Protecting and un-protecting worksheets

• Sorting tables
• Sorting tables
• Using custom sorting
• Filtering data for selected view (AutoFilter)
• Using advanced filter options

• Specifying a valid range of values for a cell
• Specifying a list of valid values for a cell
• Specifying custom validations based on formula for a cell

• Upper, Lower, Proper
• Left, Mid, Right
• Trim, Len, Exact
• Concatenate

• Basic Function –Sum, Average, Max, Min, Count, Count A
• Conditional Formatting
• Logical functions (AND, OR, NOT)
• Lookup and reference functions (VLOOKUP, HLOOKUP, MATCH, INDEX)
• V-lookup with Exact Match, Approximate Match
• Nested V-lookup with Exact Match
• V-lookup with Tables, Dynamic Ranges
• Nested V-V-lookup with Exact Match
• Using V-lookup to consolidate Data from Multiple Sheets
• Mathematical Functions
• SumIf, CountIf, AverageIf etc
• Date & Time Function

• Creating Simple Pivot Tables
• Basic and Advanced value field setting
• Grouping Based on number and Dates
• Calculated field and Calculated items

• Using Charts
• Formatting Charts
• Using 3D Graphs
• Using Bar and Line Chart together
• Using Secondary Axis in Graphs
• Sharing Charts with PowerPoint / MS Word, Dynamically

• Designing the structure of a template
• Using templates for standardization of worksheets

• Introduction to VBA
• What is VBA ?
• What can you do with VBA ?
• What can you do with VBA ?
• Procedures and Function in VBA

• What is Variables ?
• Using Non-declared variables
• Variable Data Types

• Customize Message-Box and Input-box
• Reading cell values into messages
• Various button groups in VBA

• If and Select statement
• Looping in VBA
• Mail Function –send automated email
• Automated report will be shown

• Cloud Computing Technology & its Concepts
• Comparison between On-Premise & Cloud Infrastructure
• Various Advantages of Cloud Technology
• Types of Cloud Services being offered.

• Evolution of Amazon Web Services
• E Chronology & Events of AWS Cloud
• EGlobal Clients of AWS Cloud

• A Region and Availability Zone
• About Edge Locations
• AWS Cloud Legal & Compliance Overview

• Elastic Compute Cloud Essentials
• Configure and Deploy EC2 instances.
• Types of instances offered by AWS in EC2
• Working with Amazon Machine Image
• Elastic Block Store Volumes Use Cases
• EBS based Snapshot
• Elastic IP Addressing
• Feature of Elastic Compute Cloud
• AWS Pricing & Calculating
• About Autoscaling & Use Cases

• Introduction to AWS Cloud Networking services
• Virtual Private Cloud Setup
• Public & Private Subnets Creation within a VPC
• Configuring Internet Gateway
• Network Address Translation (NAT) Gateway
• Use Case of NAT Gateway
• Establishing Connection between two VPCs through VPC Peering
• About Cloud Front and ways to Configure it.

• Simple Storage Service (S3)
• Creating S3 Bucket.
• Storages Classes in S3 Bucket
• Versioning in S3
• Static Website Hosting
• Cross Region Replication of Data through S3
• AWS Elastic File System & its Advantages
• Configuring EFS and its Use Case
• AWS Glacier

• About RDS
• Deploying RDS Instance & Configuring it.
• Amazon Dynamo DB

• Identity and Access Management (IAM)
• Creation of Users & Groups in IAM
• Authorization & Authentication for Users & Groups
• Multi-Factor Authentication using MFA Device

• Features of Route 53
• Configuring AWS Route 53

• AWS Cloud Watch
• Simple Notification Service (SNS)
• Amazon Simple Queue Service (SQS)