Artificial Intelligence Training

• Why do we need Python?
• Program structure

Execution steps

• Interactive Shell
• Executable or script files

User Interface or IDE

Memory management and Garbage collections

• Object creation and deletion
• Object properties

Data Types and Operations

• Numbers
• Strings
• List
• Tuple
• Dictionary
• Other Core Types

Statements and Syntax

• Assignments, Expressions, and prints
• If tests and Syntax Rules
• While and For Loops
• Iterations and Comprehensions

File Operations

• Opening a file
• Using Files
• Find and replace

Functions

• Function definition and call
• Function Scope
• Arguments
• Function Objects
• Anonymous Functions

Exception Handling

• Default Exception Handler
• Catching Exceptions
• Raise an exception
• User-defined exception

Advanced Concepts

Pandas Section

• Python Pandas – Introduction
• Introduction to Data Structures
• Python Pandas – Series
• Python Pandas – Data Frame
• Python Pandas – Basic Functionality
• Python Pandas – Descriptive Statistics
• Python Pandas – Indexing and Selecting Data
• Python Pandas – Function Application
• Python Pandas – Reindexing
• Python Pandas – Iteration
• Python Pandas – Sorting
• Python Pandas – Working with Text Data
• Python Pandas – Options and Customization
• Python Pandas – Missing Data
• Python Pandas – GroupBy
• Python Pandas – Merging/Joining
• Python Pandas – Concatenation
• Python Pandas – IO Tools
• Python Pandas – Comparison with SQL

Python Pandas – Dates Conversion

Data Science and AI

All the topics in data science will covered with following concept:

• Mathematics beside of each model
• Which scenario we want to use particular algorithm
• How to apply it in tool
• Inferential thing of each model
• Difference between each model
  • Introduction to Machine Learning & Predictive Modelling
  • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning

Statistics

• Standard Deviation
• Variance
• Concept of hypothesis testing
• T-test
• Chi-square
• Anova
• Correlation
• Probability
• Outliers
• Drop highly correlated features

Machine Learning

Supervised Learning – Classification

• Support vector machines classifier
• Naïve Bayes
• Nearest Neighbour
• Logistic Classification
• Decision tree Classifier
• Random forest classifier

Supervised learning -Regression

• Linear Regression
• Multiple Regression
• Polynomial Regression
• Exponential Regression
• Decision tree Regression
• Random forest Regression
• Feature selection using regression

Unsupervised Learning -Clustering

• K means clustering
• Hierarchical clustering

Ensemble method Techniques

Introduction to Natural Language Processing

Pre-processing Text

• Get Synonyms from words
• Bag of Words
• Remove Punctuation
• Remove Stop Words
• Replace Characters
• Stemming Words
• Lemmatization with Python
• Gender finder
• Strip Whitespace
• Tokenize Text
• speech tagging
• Sentence Segmentation

Convert Text to Speech in PythonDeep Learning

A subset of ML, Deep Learning (DL) is re-branding of neural networks- a class of models inspired by biological neurons in our brain.

Overview of the neural network, ANN