• Introduction to Data Science
• Need of Data Science
• BigData and Data Science’
• Data Science and machine learning
• Data Science Life Cycle
• Data Science Platform
• Data Science Use Cases
• Skill Required for Data Science

• Linear Algebra
  • Vectors
  • Matrices
• Optimization
  • Theory Of optimization
  • Gradients Descent

• Descriptive vs. Inferential Statistics
• Types of data
• Measures of central tendency and dispersion
• Hypothesis & inferences
• Hypothesis Testing
• Confidence Interval
• Central Limit Theorem

• Probability Theory
• Conditional Probability
• Data Distribution
• Distribution Functions
  • Normal Distribution
  • Binomial Distribution

• An Introduction to RDBMS & SQL
• Data Retrieval with SQL
• Pattern matching with wildcards
• Basics of sorting
• Session summary
• Order by clause
• Aggregate functions
• Group by clause
• Having clause
• Nested queries
• Inner join
• Multi join
• Outer join
• Adding and Deleting columns
• Changing column name and Data Type
• Creating Table from existing Table
• Changing Constraints Foreign key
• Introduction to Stored Procedures.

• Why Python , its Unique Feature and where to use it?
• Python environment Setup/shell
• Installing Anaconda
• Understanding the Jupyter notebook
• Python Identifiers, Keywords
• Discussion about installed module s and packages

• Python Data Types and Variable
• Condition and Loops in Python
• Decorators
• Python Modules & Packages
• Python Files and Directories manipulations
• Use various files and directory functions for OS operations

• Array Operations
• Arrays Functions
• Array Mathematics
• Array Manipulation
• Array I/O
• Importing Files with Numpy

• Built in modules (Library Functions)
• Numeric and Math’s Module
• String/List/Dictionaries/Tuple
• Complex Data structures in Python
• Python built in function
• Python user defined functions

• Data Frames
• I/O
• Selection in DFs
• Retrieving in DFs
• Applying Functions
• Reshaping the DFs – Pivot
• Combining DFs
  Merge
  Join
• Data Alignment 

• Matrices Operations
• Create matrices
  Inverse, Transpose, Trace,   Norms , Rank etc
• Matrices Decomposition
  Eigen Values & vectors
  SVDs

• Seaborn Installation
• Introduction to Seaborn
• Basics of Plotting
• Plots Generation
• Visualizing the Distribution of a Dataset
• Selection color palettes  

• Matplotlib Installation
• Matplotlib Basic Plots & it’s Containers
• Matplotlib components and properties
• Pylab & Pyplot
• Scatter plots
• 2D Plots-
• Histograms
• Bar Graphs
• Pie Charts
• Box Plots
• Customization
• Store Plots

• Basics
• Data Loading
• Train/Test Data generation
• Preprocessing
• Generate Model
• Evaluate Models

• Observations, variables, and data matrices
• Types of variables
• Measures of Central Tendency
• Arithmetic Mean / Average
  • Merits & Demerits of Arithmetic Mean and Mode
  • Merits & Demerits of Mode and Median
  • Merits & Demerits of Median Variance

• Notation and Terminology
• Unions and Intersections
• Conditional Probability and Independence

• Large Sample Test
• Small Sample Test
• One Sample: Testing Population Mean
• Hypothesis in One Sample z-test
• Two Sample: Testing Population Mean
• One Sample t-test – Two Sample t-test
• Paired t-test
• Hypothesis in Paired Samples t-test
• Chi-Square test

• Case study- Netflix
• Deep analysis on Netflix data

• Data Exploration
• Missing Value handling
• Outliers Handling
• Feature Engineering

• Importance of Feature Selection in Machine Learning
• Filter Methods
• Wrapper Methods
• Embedded Methods

• Introduction to Machine Learning
• Logistic Regression
• Naïve Bays Algorithm
• K-Nearest Neighbor Algorithm
• Decision Tress
  1. SingleTree
  2. Random Forest
• Support Vector Machines
• Model Ensemble
• Model Evaluation and performance
  • K-Fold Cross Validation
  • ROC, AUC etc…
• Hyper parameter tuning
  • Regression
  • classification

• Simple Linear Regression
• Multiple Linear Regression
• Decision Tree and Random Forest Regression

• Similarity Measures
• Cluster Analysis and Similarity Measures

• Bagging
• Boosting
• Voting
• Stacking
• K-means Clustering
• Hierarchical Clustering
• Principal Components Analysis
• Association Rules Mining & Market Basket Analysis

• collaborative filtering model
• content-based filtering model.
• Hybrid collaborative system.

• Introduction to Git and Distributed version control
• Life Cycle
• Create clone & commit Operations
• Push & Update Operations
• Stash, Move, Rename & Delete Operations

• An Introduction to Artificial Intelligence
• History of Artificial Intelligence
• Future and Market Trends in Artificial Intelligence
• Intelligent Agents – Perceive-Reason-Act Loop
• Search and Symbolic Search
• Constraint-based Reasoning
• Simple Adversarial Search (Game-Playing)
• Neural Networks and Perceptions
• Understanding Feedforward Networks
• Boltzmann Machines and Autoencoders
• Exploring Backpropagation

• Understanding Sensor Processing
• Natural Language Processing
• Studying Neural Elements
• Convolutional Networks
• Recurrent Networks
• Long Short-Term Memory (LSTM) Networks

• Natural Language Processing
• Natural Language Processing in Python
• Studying Deep Learning
• Artificial Neural Networks
• ANN Intuition
• Plan of Attack
• Studying the Neuron
• The Activation Function
• Working of Neural Networks
• Exploring Gradient Descent
• Stochastic Gradient Descent
• Exploring Backpropagation

• Understanding Artificial Neural Network
• Building an ANN
• Building Problem Description
• Evaluation the ANN
• Improving the ANN
• Tuning the ANN

• Machine Learning end to end Project blueprint
• Case study on real data after each model.
• Regression predictive modeling – E-commerce
• Classification predictive modeling – Binary Classification
• Case study on Binary Classification – Bank Marketing
• Case study on Sales Forecasting and market analysis
• Widespread coverage for each Topic
• Various Approaches to Solve Data Science Problem
• Pros and Cons of Various Algorithms and approaches
• Amazon-Recommender
• Image Classification
• Sentiment Analysis

Project Domains: Finance

• Insurance company wants to decide on the premium using various parameters of the client.
• It’s an important problem to keep the clients and attract new ones.

By completing this project you will learn:

• How to collect data?, how to justify right features? , Which ML / DL model is best in this situation? How much data is enough?
• How to have CI/CD in the project?
• How to do Deployment of Project to cloud?

Image Processing in Health care

• A hospital wants to automate Detection of pneumonia in X-rays using image processing.

By doing this project you will understand

• How to handle image data? How to preprocess and augment image data? How to choose right model for image process?
• How to apply transfer learning in image processing?
• How to do incremental learning & CI/CD in the project?
• How to do Deployment of Project to cloud?

Natural Language Processing

• One of the companies wants to automate applicant’s level in English communication.
• Create a ML/DL model for this task.

By completing this project you will learn

• How to do convert text to right representation? How to preprocess text data? How to select right ML/DL model for text data ?
• How to do transfer learning in Text Analytics?
• How to do CI/CD in text analytics project?
• How to do Deployment of Project to cloud?

Mechanical

• A mechanical company wants to perform predictive maintenance of engine parts.
• This enables company to efficiently change parts before machine fails.

By performing this task you will learn,

• How to handle time series data?
• How to preprocess time series data?
• How to create ML/DL model for Time series Data?
• How to do CI/CD in text analytics project?
• How to do Deployment of Project to cloud?

Sales / Demand Forecasting

• Predict the sales / demand of a product of a company.
• Sales / Demand forecasting of the product will help company efficiently manage the resources.
• Create a ML/DL model for this problem.

By performing this project you will learn,

• How to handle time series data?
• How to preprocess time series data?
• How to create ML/DL model for Time series Data?
• How to do CI/CD in text analytics project?
• How to do Deployment of Project to cloud?