Complete AI Course With 9 Months Internship

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Complete AI Course With 9 Months Internship

Complete AI Course With 9 Months Internship

Level : Intermediate

Price : 149999   95999

12 Month(s)

Designed by IIT Chennai Alumni

2-Year Course (Includes 9 Months Stipend-Based Industrial Training), 

Online Course + Live Classes + Test Series

Are you fascinated by the world of Artificial Intelligence (AI)? Our comprehensive course is designed to help you explore AI's remarkable potential and pave the way for your future success.

Learn from Industry Experts

Our course is developed by esteemed alumni of IIT Madras, bringing together a team of industry veterans with vast experience in AI research and development. Gain unparalleled insights and knowledge from these experts as you embark on your AI learning journey.

Theory + 10 Live Projects

Dive into a blend of theoretical learning and hands-on projects to gain a thorough understanding of AI principles and techniques. Engage with 20 live projects, carefully selected to cover a diverse array of industries and real-world applications.

Modules Covered

Our course offers an extensive curriculum that includes key AI concepts:

  1. Python
  2. Artificial Intelligence (AI)
  3. Machine Learning
  4. Deep Learning
  5. R Programming
  6. Data Science
  7. Advanced Neural Networks

Each module is designed to provide you with a deep understanding of both theoretical foundations and practical applications.

Practical Learning

Apply what you learn in the classroom to real-world scenarios through hands-on projects and interactive exercises. Develop the skills and confidence needed to tackle complex AI challenges and drive innovation in your chosen field.

Suitable for All Ages

Our course is tailored for students, professionals, and AI enthusiasts of all ages and backgrounds. No matter your experience level, you'll find valuable insights and resources to accelerate your AI journey.

Who Can Benefit?

Students:

  • Enhance your academic studies with practical AI skills and knowledge that will set you apart from your peers.
  • Gain a competitive edge in your future career by mastering AI concepts that are in high demand across various industries.
  • Get hands-on experience through real-world projects to prepare you for professional challenges.

Professionals:

  • Upscale your career with confidence by acquiring the expertise needed to excel in the rapidly evolving AI field.
  • Stay ahead of industry trends and advancements by learning cutting-edge AI skills.
  • Transition smoothly into AI roles with our comprehensive course designed to bridge the gap between your current skills and industry requirements.

AI Enthusiasts:

  • Fuel your curiosity and passion for AI by exploring its fascinating concepts and applications.
  • Discover innovative ways to apply AI to art, music, technology, and more.
  • Join a supportive community of learners, exchange ideas, collaborate on projects, and embark on your AI journey together.

Stipend-Based Industrial Training

In addition to the study program, our course includes 9 months of stipend-based industrial training. Gain real-world experience, apply your knowledge in a professional setting, and earn a stipend while you learn. This unique opportunity ensures you are job-ready and equipped with practical skills to thrive in the AI industry.

Start Your AI Journey Today

Join us and begin an exciting adventure into the world of Artificial Intelligence. Whether you're a beginner seeking to grasp the basics or a seasoned professional looking to expand your skill set, our comprehensive course has something for everyone. Together, let's shape the future of AI and harness its potential to drive positive change in the world.

Ready to take the first step? Enrol now and unleash the power of Artificial Intelligence!

Topics:

Module 1 Python

1.      Introduction

2.     Installation of Python

3.     Working with Python

4.    Variables

5.     List

6.     Tuple

7.     Set

8.     Strings

9.     Dictionary

10.  More on Variables

11.    Data Types

12.   Operators

13.   Binary System

14.  Swapping of Two Numbers

15.   Bitwise Operator

16.   Mathematical Functions in Python

17.   How to take Input Value from the user?

18.   If Else statement

19.   While loop

20. For loop

21.   Break, Continue and Pass

22.  Patterns

23.  For-Else

24. Prime numbers

25.  Array

26.  Use input in Array

27.  NumPy

28.  Creating Array using NumPy

29.  Creating other Array from existing ones

30. Matrix in Python

31.   Functions in Python

32.  Types of Parameters

33.  Kwargs

34. Global & Local Variables

35.  Fibonacci Series

36.  Factorial

37.  Recursion

38.  Using Recursion

39.  Lambda Function

40. Filter() Map() Reduce()

41.  Decorators

42. Modules in Python

43. OOPS

44. Class and Object

45. Types of Methods

46. Inner Class

47. Inheritance

48. Types of Inheritance

49. Constructors in Inheritance

50. Init function in Inheritance

51.   Duck Typing

52.  Operator Overloading

53.  Polymorphism

54. Iterators

55.  Exception Handling

56.  Method Over-riding

57.  Multi-Threading

58.  Linear Search

59.  Binary Search

60. Bubble Set

61.   Selection Sort

62.  Generators

63.  Init Method

64. Operation on Array

65.  Solving Algebraic Equations

66. Super Function

67.  Types of Variables

68. How to write Comments?

69. Project 1- Birthday Reminder

70. Project 2- Tic-Tac-Toe game


Module 2  Artificial Intelligence

1. History of AI

2.  What is AI? 3.Applications of AI

4.  Types of Artificial Intelligence

5. Programming languages of artificial intelligence

6.  Introduction to Python

7.  Introduction to Machine Learning

8.  Need for Machine Learning

9.  Machine Learning Process

10.   Types of Machine Learning

11.   Problems which can be solved by using machine learning

12.   Surprised learning Algorithms

13. Linear Regression

14. Linear Regression in Python 15.logistic Regression

16.  Categorical & Numerical Variable

17.  Decision Tree

18.  Decision Tree Algorithm 19.Informationgain & entropy

20.  Random Forest

21.    Creating a Random Tree

22.  Navie Bayes

23.  SVM

24. K Means Clustering

25.  K nearest Neighbor

26.  Reinforcement learning

27.  Terminologies used in RL

28.  Reward Maximization

29.  Markov’s Decision Q Learning

30.  AI VS ML VS DL

31.  Why deep learning

32.  What is deep learning

33.  Single layer Perceptron

34.  Multiple layer Perceptron

35.  Neural Network

36.  Backpropagation

37.  Overfitting and Underfitting 38.Trianing Neural Network

39.  Limitations of feed forward Networks

40.  RNN

40.  CNN

41.  Text Mining and NLP

42.  Natural language Processing

43.  Applications of NLP

44.  Important Terms

Module 3  Machine learning Part 1

1.  Introduction to Machine Learning

2.  Need for Machine Learning

3.  Machine Learning Process

4.  Types of Machine Learning

5.  Problems which can be solved by using machine learning

6.  Surprised learning Algorithms

7.  Decision Tree

8.  Decision Tree Algorithm 9.Information gain& entropy

10.  Random Forest

11.    Creating a Random Tree

12.  Navie Bayes

13.  SVM

14. K Means Clustering

15.  K nearest Neighbor

16.  Reinforcement learning

17.  Terminologies used in RL

18.  Reward Maximization

19.  Markov’s Decision

20.  Understanding Q learning

21.  AI VS ML VS DL

22.  Overfitting & Underfitting

23. RNN

Module 4  Machine learning Part 2 (Probability)

1.   Introduction to Probability

2.   How to calculate expected values?

3.   Events & their Complements

4.   Combinatorics

5.   Permutation

6.   Operations with factorial

7.   Variations

8.   Variations without repetition

9.   Combination

10.    Sets & Elements

11.    How can sets interact?

12.    Intersection of sets

13.    Union of sets

14.    Mutually Exclusive Set

15.    Independent & Dependent Events

16.    Conditional Probability

17.    Bayes law

18.    Baye’s Theorem Example

19.    Total probability law

20.    Addition Rule

21.    Multiplication Rule

22.    Probability Distributions Fundamental

23.    Types of Probability Distributions Fundamental Part 1

24.    Types Probability Distributions Fundamental Part 2

25.    Features of Continuous Distributions

26.    Features of Discreet Distributions

27.    Union Distributions

28.    Bernoulli Distributions

29.    Binomial Distributions

30.    Poisson Distributions

31.    Normal Distributions

Module 5  Machine learning Part 3 (Statistics)

1.    Population and Sample

2.    Types of data

3.    Measurement levels

4.    Categorical and Numerical Variables

5.    Histogram

6.    Mean, Median and Mode

7.    Skewness

8.    Variance

9.    Standard Deviation

10. Correlation Coefficient

11. Introduction to Distribution

12. Standard Normal Distribution

13. Central Limit Theorem

14. Estimator and Estimate

15. Confidence Interval

16. Students T Distribution

17. Null and Alternative Hypothesis

18. Type 1 and Type 2 Error

19. P value

Module 6  Machine learning Part 4 (Advanced Statistics)

 1.  Regression Analysis

2.  Correlation Vs Regression

3.  Python Packages Installation

4.  How to install Jupiter Notebook

5.  First Linear Regression in Python

6.  Decomposition of Variability

7.  OLS

8.  R Squared

9.  Multiple Linear Regression

10.   Adjusted R Squared

11.   Linearity

12.   Logistic Regression

13.   Logistic vs Logit Function

14.   Overfitting and Underfitting

15.   Cluster Analysis

16.   Classification vs Clustering

17.   K means Clustering

18.   Choose the value of K number of clustering.

19.   K means Clustering Limitations

20.   Types of Clustering

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