The artificial intelligence and machine learning course , you will get to learn about the:

•  What is Machine Learning? 
•  Python for AI 
•  Machine Learning Model 
•  Deep Learning. 
•  Reinforment Learning 
•  Sequential Learning 
•  Supervised Learning 
•  Regression Testing

You should have a basic understanding of Computer Programming terminologies. A basic understanding of Data analytics and machine learning.
Who Should Attend:

•  Bachelor’s Degree preferably in a Mathematical/ Statistical background. If you have any prior experience in programming/ analytics. 
•  Ideal for Engineers, Software/IT/Data Professionals looking to boost their careers. 

In preparation to earn the Red Hat Certified System Administrator (RHCSA), Red Hat recommends the following training modules:

• Red Hat System Administration I (RH124)
• Red Hat System Administration II (RH134)

Certification Exam : Test your skills and knowledge

 EX200 – Red Hat Certified System Administrator (RHCSA) exam

• The performance-based Red Hat Certified System Administrator (RHCSA) exam (EX200) tests your knowledge and skill in areas of system administration common across a wide range of environments and deployment scenarios. You must be an RHCSA to earn a Red Hat Certified Engineer (RHCE®) certification.

Module 1: Python for AI and ML

Python Basics

• Introduction
• Variable Declaration
• Conditional Construct
• Function
• Array Manipulation
• Working with List , Tuples and dictionaries
• Exception Handling
• Event processing with Threading
• Introduction of tkinter library
• Working with widgets
• Connectivity with PyMysql using Python

Python OOPs

• Classes and Object
• Inheritance and Overriding
• Encapsulation
• File Handling

Introduction to NumPy

Pandas

• Data Pre-processing
• Data Manipulation
• Data Loading using Difference Storage
• Data Joining

• ❖ Data Visualization Introductions of matplotlib
• Defining Charts and plots
• Introduction of Figure Class

❖ Data Visualization using Seaborne

❖ Data Science

Module 2: Predictive Analytics

• ❖ Statistical Foundations
• ❖ Exploratory Data Analysis (EDA)
• ❖ Probability
• ❖ Inferential Statistics
• ❖ Regression- Linear Regression
• ❖ Regression- Logistic Regression
• ❖ Advance Logistic Regression

Module 3 Machine Learning

• ❖ Introduction to Machine Learning What is Machine Learning?

• OpenCV Library
• Scikit Learn Library
• Linear Regression Algorithm
• Decision Tree
• Random Forest
• Regression Analysis open
• link Linear Regression Simple Linear Regression
• Multiple Linear
• Regression Backward Elimination
• Polynomial Regression

• ❖ Supervised Learning – Classification I
• ❖ Supervised Learning – Classification II
• ❖ Model Selection and Boosting
• ❖ Unsupervised Learning
• ❖ Dimensionality Reduction
• ❖ Association Rules Mining & Recommendation Systems

Module 4: Deep Learning

• ❖ Introduction to Deep Learning • Introduction of natural Network

❖ Getting Started with TensorFlow 2.0

❖ Convolutional Neural Network

❖ Regional CNN

❖ Boltzmann Machine &Autoencoder

❖ Generative Adversarial Network

❖ Emotion and Gender Detection

❖ YOLO Object Detection

Module 5: Sequential Learning

• ❖ Introduction to Sequence Learning
• ❖ Name Entity Recognition using CRF
• ❖ RNN and GRU
• ❖ LSTM
• ❖ Auto Image Captioning
• ❖ Machine Translation

Module 6: Reinforcement Learning

• ❖ Introduction to Reinforcement Learning
• ❖ Bandit Algorithms and Markov Decision Process
• ❖ Dynamic Programming & Temporal Difference Learning methods
• ❖ Deep Q Learning

IT and ML – https://iabac.org/artificial-intelligence-certification

IT and ML – https://apmg-international.com/product/artificial-intelligence-ai

IT and ML – https://www.starcertification.org/

IT and ML – https://www.artiba.org/certification/artificial-intelligence-certification