• Value Chain
• Analytics vs Data Science
• Types of Analytics
• Lifecycle Probability
• Analytics Project Lifecycle
• Advantage of Deep Learning over Machine learning
• Reasons for Deep Learning
• Real-Life use cases of Deep Learning
• Review of Machine Learning

Data

• Basis of Data Categorization
• Types of Data
• Data Collection Types
• Forms of Data & Sources
• Data Quality & Changes
• Data Quality Issues
• Data Quality Story
• What is Data Architecture
• Components of Data Architecture
• OLTP vs OLAP
• How is Data Stored?

• What is Big Data?
• 5 Vs of Big Data
• Big Data Architecture
• Big Data Technologies
• Big Data Challenge
• Big Data Requirements
• Big Data Distributed Computing & Complexity
• Hadoop
• Map Reduce Framework
• Hadoop Ecosystem

• What Data Science is
• Why Data Scientists are in demand
• What is a Data Product
• The growing need for Data Science
• Large Scale Analysis Cost vs Storage
• Data Science Skills
• Data Science Use Cases
• Data Science Project Life Cycle & Stages
• Data Acuqisition
• Where to source data
• Techniques
• Evaluating input data
• Data formats
• Data Quantity
• Data Quality
• Resolution Techniques
• Data Transformation
• File format Conversions
• Annonymization

• Python Overview
• About Interpreted Languages
• Advantages/Disadvantages of Python pydoc.
• Starting Python
• Interpreter PATH
• Using the Interpreter
• Running a Python Script.
• Using Variables
• Keywords
• Built-in Functions
• StringsDifferent Literals
• Math Operators and Expressions
• Writing to the Screen
• String Formatting
• Command Line Parameters and Flow Control.
• Lists
• Tuples
• Indexing and Slicing
• Iterating through a Sequence
• Functions for all Sequences

• The xrange() function
• List Comprehensions/li>
• Generator Expressions
• Dictionaries and Sets.

Numpy & Pandas

• Learning NumPy
• Introduction to Pandas
• Creating Data Frames
• GroupingSorting
• Plotting Data
• Creating Functions
• Slicing/Dicing Operations.

Deep Dive – Functions & Classes & Oops

• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values. Sorting
• Alternate Keys
• Lambda Functions
• Sorting Collections of Collections.
• Classes & OOPs.

• What is Statistics
• Descriptive Statisticst
• Central Tendency Measures
• The Story of Average
• Dispersion Measuresb
• Data Distributions
• Central Limit Theorem
• What is Sampling
• Why Sampling
• Sampling Methods
• Inferential Statistics
• What is Hypothesis testing
• Confidence Level
• Degrees of freedom
• what is pValue
• Chi-Square test
• What is ANOVA
• Correlation vs Regression
• Uses of Correlation & Regression

• ML Fundamentals
• ML Common Use Cases
• Understanding Supervised and Unsupervised Learning Techniques

Clustering

• Similarity Metrics
• Distance Measure Types: Euclidean, Cosine Measures
• Creating predictive models
• Understanding K-Means Clustering
• Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
• Case study

Implementing Association rule mining

• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Recommendation Use-case
• Case study

Understanding Process flow of Supervised Learning Techniques

• How to build Decision trees

Decision Tree Classifier

• What is Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Decision Tree
• Confusion Matrix
• Case study

Random Forest Classifier

• What is Random Forests
• Features of Random Forest
• ut of Box Error Estimate and Variable Importance
• Case study

Problem Statement and Analysis

• Pros and Cons of different approaches and algorithms.

Linear Regression

• Case study.
• Introduction to Predictive Modeling
• Linear Regression Overview
• Simple Linear Regression
• Multiple Linear Regression

Logistic Regression

• Case study.
• Logistic Regression Overview
• Data Partitioning
• Univariate Analysis
• Bivariate Analysis
• Multicollinearity Analysis.
• Model Building
• Model Validation
• Model Performance Assessment AUC & ROC curves
• Scorecard

• Introduction to Spark Core
• Convolutional Neural Networks
• Working with RDDs
• Introduction to PySpark
• Machine learning with PySpark – Mllib

Deep Learning & AI using Python

• Case study.

• The Detailed ANN
• The Activation Functionss
• How do ANNs work & learn
• Gradient Descent
• Stochastic Gradient Descent
• Backpropogation
• Understand limitations of a Single Perceptron
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• Backpropagation – Learning Algorithm
• Understand Backpropagation – Using Neural Network Example
• MLP Digit-Classifier using TensorFlow
• Building a multi-layered perceptron for classification
• Why Deep Networks
• Why Deep Networks give better accuracy?
• Use-Case Implementation
• Understand How Deep Network Works?
• How Backpropagation Works?
• Illustrate Forward pass, Backward pass
• Different variants of Gradient Descent

Convolutional Neural Networks

• Convolutional Operation
• Relu Layers.
• What is Pooling vs Flattening
• Full Connection
• Softmax vs Cross Entropy
• for image classification

• Recurrent neural networks rnn
• LSTMs understanding LSTMs
• long short term memory neural networks lstm in python

Restricted Boltzmann Machine (RBM) and Autoencoders

• Restricted Boltzmann Machine
• Applications of RBM
• Introduction to Autoencoders
• Autoencoders applications
• Understanding Autoencoders
• Building a Autoencoder model

• Introducing Tensorflow
• Why Tensorflow?
• What is tensorflow?
• Tensorflow as an Interface
• Tensorflow as an environment
• Tensors
• Computation Graph
• Installing Tensorflow
• Tensorflow training
• Prepare Data
• Tensor types
• Loss and Optimization
• Running tensorflow programs
• Tensors
• Tensorflow data types
• CPU vs GPU vs TPU
• Tensorflow methods
• Introduction to Neural Networks
• Neural Network Architecture
• Linear Regression example revisited
• The Neuron
• Neural Network Layers
• The MNIST Dataset
• Coding MNIST NN
• Images and Pixels
• How humans recognise images
• Convolutional Neural Networks
• ConvNet Architecture
• Overfitting and Regularization
• Max Pooling and ReLU activations
• Dropout
• Strides and Zero Padding
• Coding Deep ConvNets demo
• Debugging Neural Networks
• Visualising NN using Tensorflow
• Tensorboard
• Transfer Learning Introduction
• Google Inception Model
• Retraining Google Inception with our own data demo
• Predicting new images
• Transfer Learning Summary
• Extending Tensorflow
• Keras
• TFLearn
• Keras vs TFLearn Comparison