• Understanding Data Science
• The Data Science Life Cycle
• Understanding Artificial Intelligence (AI)
• Overview of Implementation of Artificial Intelligence
• Machine Learning
• Deep Learning
• Artificial Neural Networks (ANN)
• Natural Language Processing (NLP)
• How Python connected to Machine Learning
• Python as a tool for Machine Learning Implementation

• What is Python and history of Python
• Python-2 and Python-3 differences
• Install Python and Environment Setup
• Python Identifiers, Keywords, and Indentation
• Comments and document interlude in Python
• Command-line arguments and Getting User Input
• Python Basic Data Types and Variables

• Understanding Lists in Python
• Understanding Iterators
• Generators, Comprehensions and Lambda Expressions
• Understanding and using Ranges

• Introduction to the section
• Python Dictionaries and More on Dictionaries
• Sets and Python Sets Examples

• Reading and writing text files
• Appending to Files
• Writing Binary Files Manually and using Pickle Module

• Python user defined functions
• Python packages functions
• The anonymous Functions
• Loops and statement in Python
• Python Modules & Packages

• What is Exception?
• Handling an exception
• try….except…else
• try-finally clause
• The argument of an Exception
• Python Standard Exceptions
• Raising an exceptions
• User-Defined Exceptions

• What are regular expressions?
• The match Function and the Search Function
• Matching vs Searching
• Search and Replace
• Extended Regular Expressions and Wildcard

• Collections – named tuples, default dicts
• Debugging and breakpoints, Using IDEs

• Understanding different types of Data
• Understanding Data Extraction
• Managing Raw and Processed Data
• Wrangling Data using Python
• Using Mean, Median and Mode
• Variation and Standard Deviation
• Probability Density and Mass Functions
• Understanding Conditional Probability
• Exploratory Data Analysis (EDA)
• Working with Numpy, Scipy and Pandas

• Understand what is a Machine Learning Model
• Various Machine Learning Models
• Choosing the Right Model
• Training and Evaluating the Model
• Improving the Performance of the Model

• Understanding Predictive Model
• Working with Linear Regression
• Working with Polynomial Regression
• Understanding Multi Level Models
• Selecting the Right Model or Model Selection
• Need for selecting the Right Model
• Understanding Algorithm Boosting
• Various Types of Algorithm Boosting
• Understanding Adaptive Boosting

• Understanding the Machine Learning Algorithms
• Importance of Algorithms in Machine Learning
• Exploring different types of Machine Learning Algorithms
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

• Understanding the Supervised Learning Algorithm
• Understanding Classifications
• Working with different types of Classifications
• Learning and Implementing Classifications
• Logistic Regression
• Naïve Bayes Classifier
• Nearest Neighbour
• Support Vector Machines (SVM)
• Decision Trees
• Boosted Trees
• Random Forest
• Time Series Analysis (TSA)
• Understanding Time Series Analysis
• Advantages of using TSA
• Understanding various components of TSA
• AR and MA Models
• Understanding Stationarity
• Implementing Forecasting using TSA

• Understanding Unsupervised Learning
• Understanding Clustering and its uses
• Exploring K-means
• What is K-means Clustering
• How K-means Clustering Algorithm Works
• Implementing K-means Clustering
• Exploring Hierarchical Clustering
• Understanding Hierarchical Clustering
• Implementing Hierarchical Clustering
• Understanding Dimensionality Reduction
• Importance of Dimensions
• Purpose and advantages of Dimensionality Reduction
• Understanding Principal Component Analysis (PCA)
• Understanding Linear Discriminant Analysis (LDA)

• What is Hypothesis Testing in Machine Learning
• Advantages of using Hypothesis Testing
• Basics of Hypothesis
• Normalization
• Standard Normalization
• Parameters of Hypothesis Testing
• Null Hypothesis
• Alternative Hypothesis
• The P-Value
• Types of Tests
• T Test
• Z Test
• ANOVA Test
• Chi-Square Test

• Understanding Artificial Intelligence
• Understanding Machine Learning
• Understanding the need for Deep Learning for Machines
• Understanding Deep Learning
• Understanding the Importance of Neural Network
• Understanding how Artificial Intelligence, Machine Learning and Deep Learning are related
• Introduction to Deep Learning Frameworks
• Introduction to Tensorflow and Keras

• Understanding Reinforcement Learning Algorithm
• Advantages of Reinforcement Learning Algorithm
• Components of Reinforcement Learning Algorithm
• Exploration Vs Exploitation tradeoff

• Installing Tensorflow
• Installing Keras
• Understanding Deep Learning Environment in Cloud Platform with AWS
• Executing Tensorflow Code
• Executing Tensorflow in AWS

• Understanding Placeholders
• Creating Placeholders
• Updating Placeholders with Data
• Understanding Variables and Constants
• Understanding Computation Graph
• Exploring Tensor Board
• Understanding Functions in Tensorflow
• Exploring various Key Functions
• Activation Functions
• Sigmoid Functions and Softmax Functions
• Understanding Rectified Linear Units – ReLu and Hyperbolic Tangent Functions

• Understanding what is Convolutional Neural Networks
• Understanding the Architecture of CNN
• Understanding the Convolutional Layers
• Understanding the Pooling Layer
• Understanding the Normalization Layer
• Understanding the Fully-Connected Layer
• Understanding various Popular CNN Architectures and Models
• Exploring the Imagenet Dataset
• Understanding MLP Vs CNN
• Application Areas of CNN
• Working Examples for CNN using Tensorflow and Keras

• Understanding Sequences
• Need for Neural Networks to Handle Sequences
• Understanding Recurrent Neural Networks – RNN
• Understanding the Recurrent Neuron
• Managing Forward Propagation and Back Propagation in a RNN
• Exploring various RNN Architectures
• Application Areas of RNN
• Working Examples for RNN using Tensorflow and Keras

• Understanding a Neural Network
• Understanding the Components of a Neural Network
• Input Layers
• Computational Layers
• Output Layers
• Understanding Forward Propagation and Back-Propagation
• Understanding the Hyper Parameters
• Understanding Perceptron
• Understanding Inputs and Weights
• Understanding Outputs
• Understanding Multi Layered Perceptron (MLP)
• Understanding and implementing Regularization
• Training Neural Networks
• Understanding Training Data Sets
• Understanding and using the MNIST Data Set
• Application Areas of MLP
• Working examples for MLP using Tensorflow and Keras

• Understanding Recursive Neural Networks
• Understanding the differences between Recurrent and Recursive Neural networks
• Application areas of Recursive Neural Networks
• Working Examples for Recursive Neural Networks using Tensorflow and Keras