Artificial Intelligence (Ai)

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Artificial Intelligence (AI)

Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications
For IT & Computer science Engineers

Course Type :

₹ 21788

Introduction to AI: History and Applications
Statistic Essentials and Pre-program Preparation
Python Programming Basics: Control Flow, Functions
Data Structures, Modules, and File I/O
Handling Exceptions and Using Standard Library

Supervised Machine Learning Algorithms
Image and Video Processing with OpenCV
Robotics and Raspberry Pi Integration
Text Mining with NLTK: Tokenization, Stemming, Lemmatization
Deep Learning and Neural Networks

Course Content

Topic 1 | History Of AI

Session 1 | History Of AI

Topic 2 | What is Artificial Intelligence

Topic 3 | AI Application

Topic 4 | Supervised algorithm for AI problem

Topic 5 | Programming Languages For AI

Topic 6 | Machine Learning

Topic 7 | Types Of Machine Learning

Topic 8 | Supervised Learning Algorithms

Topic 9 | Decision Tree

Topic 10 | Random Forest

Topic 11 | Naïve Bayes Classifier

Topic 12 | K Nearest Neighbor

Topic 13 | Support Vector Machines

Topic 14 | Reinforcement Learning

Topic 15 | Limitations Of Machine Learning

Topic 16 | Deep Learning

Topic 17 | Key Concepts in Text Processing

Topic 18 | Natural Language Processing

Course Description

Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. AI is currently one of the hottest buzzwords in tech and with good reason. The last few years have seen several innovations and advancements that have previously been solely in the realm of science fiction slowly transform into reality.

Experts regard artificial intelligence as a factor of production, which has the potential to introduce new sources of growth and change the way work is done across industries. For instance, this PWC article predicts that AI could potentially contribute $15.7 trillion to the global economy by 2035. China and the United States are primed to benefit the most from the coming AI boom, accounting for nearly 70% of the global impact.

Course objectives

All complex real-world problems like self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection are solved with Artificial Intelligence (AI). The main objective of this course is to give a comprehensive knowledge of the basic techniques for creating intelligent computer systems and a perception of how AI is applied to problems by introducing AI’s key problems, and the state-of-the-art models and algorithms used to undertake these problems.

Roles in industry

What IT jobs will be hot in the age of AI? Take a sneak peek at roles likely to be in demand

Intelligence Designer

Data Curator

Data Evangelist

Machine Learning Data Scientist

Robotics Process Analyst

Digital Knowledge Manager

AI Interaction Designer

Cognitive Copywriter

Course Highlights

Artificial Intelligence

Statistics Essentials

Pre-Program Preparation

Python

Machine learning

OPEN-CV

Natural Language Processing

Deep Learning

TensorFlow

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Artificial Intelligence (AI)

Course Content

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Courses

Artificial Intelligence (AI)

Course Content

Session 1 | History Of Ai (2 lectures)

Session 2 | What Is Artificial Intelligence (2 lectures)

Session 3 | Ai Application (2 lectures)

Session 4 | Type Of Ai (2 lectures)

Session 5 | Programming Languages For Ai (2 lectures)

Session 6 | Machine Learning (2 lectures)

Session 7 | Types Of Machine Learning (2 lectures)

Session 8 | Supervised Learning Algorithms (3 lectures)

Session 9 | Decision Tree (2 lectures)

Session 10 | Random Forest (2 lectures)

Session 11 | Naïve Bayes Classifier (2 lectures)

Session 12 | K Nearest Neighbor (2 lectures)

Session 13 | Support Vector Machines (2 lectures)

Session 14 | Reinforcement Learning (2 lectures)

Session 15 | Limitations Of Machine Learning (2 lectures)

Session 16 | Deep Learning (2 lectures)

Session 17 | Neural Networks (3 lectures)

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Quick Links

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