Combo Masters Program in Data Science and Artificial Intelligence

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 Hierarchial clustering, K means clustering using clustering examples and know what clustering machine learning is all about.

Topics

Hierarchial

• 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 Neighbour
  • Complete Linkage / Farthest Neighbour
  • 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 neighbour 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, Varience, Standard Deviation and Graphical representations in R
  • Creating Python Objects
  • Practice Mean, Median, Varience, 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

• 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)

Introduction to R Programming

• Introduction to R
• Data Types in R

How To Install R & R Studio

Data Structures in R

• Variable in R
• R-Overview
  • Vector
  • Matrix
  • Array
  • List
  • Data-Frame
• Operators in R
  • arithmetic
  • Relational
  • Logical
  • Assignment
  • Miscellaneous
• Conditiional 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

Programming Statistical

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

How to Import Dataset in R

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

R-Packages

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

How to Integrate R and SQL

How to Get Data From SQL to R

Introduction to python

Description: You will get an introduction to the Python programming language and understand the importance of it. How to download and work with Python along with all the basics of Anaconda will be taught. You will also get a clear idea of downloading the various Python libraries and how to use them.

Topics

• Introduction to Python
• Installation of Anaconda Python
• Difference between Python2 and Python3
• Python Environment
• Operators
• Identifiers
• Exception Handling (Error Handling)

Basic Python

Description: In this module, you will learn the basics such as assigning a variable, differences between dictionary, sets, tuple, and some decision making statements. Also, you will learn about working with different loops, data types and its usage.

Topics

• Data Types
• Conditional Statements
• Functions
• Loops

Working with libraries like NumPy, Pandas, Matplotlib, Seaborn, SciPy, Sklearn In Python

Description: This module helps you to learn and understand the different libraries used in Python. You will also get a clear idea about the NumPy library and how you can use it. NumPy is a Numeric Python library which helps in dealing with the numeric calculations with data frames.

Topics

• NumPy Introduction
• Arrays
• Array Indexing
• NumPy Data Types
• Treating Missing and NA’s
• Reshaping and combining Arrays

Working experience with Pandas In Python

Description: In this module, you will learn how to download the Pandas package and syntax for the same. Pandas is also a library similar to Numpy which predominantly helps in working with series data and data frames. You will learn how to impute the data in the place of missing values called the missing value treatment done in the Pandas package itself.

Topics

• Pandas Introduction
• Basic Operations on Series
• Dataframe
• Working with Text Data
• Working with Missing Data
• Indexing and Selecting Data
• Merge, Join and Concatenate

Working experience with Matplotlib library In Python

Description: In this module, you will learn where, how and when to use the Matplotlib library. This library is used to visualize the data. You will get an in-depth understanding of the importance of this library.

Topics

• Introduction to Matplotlib
• Matplotlib design and different visualizations

Description: This module will help you to understand the importance of Seaborn package and downloading the library Just like the Matplotlib library, the Seaborn library is also used in visualizing the data allowing high-level visualizations with categorical data.

Topics

• Introduction to Seaborn Library
• Visualizing the Distribution of the Datasets
• Plotting the Categorical Data
• Visualizing Linear Relationships
• Visualizing Statistical Relationships

To work with Seaborn Library (High-level interface for drawing attractive and informative statistical graphics) In Python

Description: This module will help you to understand the importance of Seaborn package and downloading the library Just like the Matplotlib library, the Seaborn library is also used in visualizing the data allowing high-level visualizations with categorical data.

Topics

• Introduction to Seaborn Library
• Visualizing the Distribution of the Datasets
• Plotting the Categorical Data
• Visualizing Linear Relationships
• Visualizing Statistical Relationships

Introduction to SciPy and Sklearn Libraries In Python

Description: In this module, you will understand the importance of both Scipy and Sklearn libraries which are predominantly used in building Machine Learning Algorithms working with Linear Equations. Sklearn also known as Scikit-learn, is a machine learning library for the Python programming language. You will get a clear idea of where you can use these libraries along with some examples.

Topics

• Installing both SciPy and Sklearn Libraries
• Introduction to SciPy (Mathematical Algorithms)
• Introduction to Sklearn (Machine Learning Algorithms)

• 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 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

What is Data Visualization?

• 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

Tableau – Data Visualization Tool

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

Tableau User Interface

• Understanding about Data Types and Visual Cues

Basic Chart types

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

Intermediate Chart

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

Advanced Charts

• 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)

Maps in Tableau

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

Adding Background Image

• 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

Data Connectivity in-depth understanding

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

Creating Calculated Fields

• 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

Responsive Tool Tips

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

Connecting Tableau with Tableau Server

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

Connecting Tableau with R

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

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 & Limiltation]
• 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 & Hysterisis
• 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
• Backpropogation through time
• Different Type of RNNs
• GRU
• LSTM
• Biderectional LSTM
• Deep RNN
• Word Embedding
• Debiasing
• Negative Sampling
• Elmo & Bert
• Beam Search
• Attention Model

• Autoencoders & Decoders
• Adversial 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