Artificial Intelligence Complete Course

Artificial Intelligence Complete Course

Artificial Intelligence Complete Course

Level : Basic

Price : 74999   49999

5 Month(s)

Designed by IIT Madras Alumni

Are you curious about Artificial Intelligence (AI)? Our course is perfect for anyone who wants to learn about AI and its amazing possibilities.

Learn from Industry Experts

Developed by distinguished alumni from the prestigious IIT Madras, our course is curated by industry veterans with extensive experience in AI research and development. Benefit from their wealth of knowledge and practical insights as you embark on your AI journey.

Theory + 20 Live Projects

Gain a holistic understanding of AI principles and techniques through a blend of theoretical learning and hands-on projects. Immerse yourself in real-world AI applications with our curated selection of 20 live projects, covering a diverse range of industries and use cases.

Modules Covered: 

Our course covers a wide array of key AI concepts, including :

1. Python

2. AI

3. Machine Learning, 

4. Deep Learning

5. R

Each module is meticulously crafted to provide you with a comprehensive understanding of the underlying principles and practical applications.

Practical Learning

Apply theoretical concepts to real-world scenarios with our practical learning approach. Through hands-on projects and interactive exercises, you'll develop the skills and confidence to tackle complex AI challenges and drive innovation in your chosen domain.

Suitable for All Ages

Whether you're a student, a seasoned professional, or simply an AI enthusiast, our course is tailored to meet your learning needs. Regardless of your background or experience level, you'll find valuable insights and resources to accelerate your AI journey.

Who Can Benefit?

Students:

  • Enhance Your Learning: Dive deeper into the world of Artificial Intelligence and complement your academic studies with practical skills and knowledge that will set you apart from your peers. 
  • Gain a competitive edge in your future career by mastering AI concepts and techniques that are increasingly in demand across various industries. 
  • Get hands-on experience through real-world projects that will not only strengthen your understanding but also prepare you for the challenges of the professional world.

Professionals:

  • Upscale with Confidence! Whether you're already working in a related field or looking to switch careers, our course provides you with the expertise and confidence to excel in the rapidly evolving field of AI. 
  • Keep pace with industry trends and advancements by acquiring cutting-edge AI skills that will make you a valuable asset to any organization. 
  • Make a smooth transition into the AI industry with our comprehensive course, designed to bridge the gap between your current skills and the requirements of AI roles.

Any AI Enthusiast Learner:

  • Explore your passion and fuel your curiosity and passion for AI by delving into its fascinating concepts and applications, from machine learning algorithms to advanced neural networks. 
  • AI offers endless possibilities for innovation and discovery. Whether you're interested in art, music, or technology, our course will inspire you to explore new ways of using AI to express your creativity. 
  • Join a Community of Learners. Connect with fellow enthusiasts and experts in our supportive learning community, where you can exchange ideas, collaborate on projects, and embark on your AI journey together.

Start Your AI Journey Today

Join us and embark on an exciting adventure into the realm of Artificial Intelligence. Whether you're a beginner seeking to understand the fundamentals 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? Enroll 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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