Course Curriculum

    1. Pre Test

    1. Unit 1: The Concept of Artificial Intelligence

    2. 1.1 Definition of Artificial Intelligence

    3. 1.2 Types and Subsets of Artificial Intelligence

    4. 1.3 Definition of Machine Learning

    5. 1.4 Disciplines Related to Machine Learning

    6. 1.5 Types and Choices of Machine Learning-based Data Analysis

    7. 1.6 Procedures for Machine Learning-based Data Analysis

    8. 1.7 Reasons For Machine Learning

    9. 1.8 Limitations of Machine Learning

    10. Unit 2: Applications of AI

    11. 2.1 Applications of Artificial Intelligence

    12. 2.2 Image Recognition

    13. 2.3 Computer Vision & Machine Vision

    14. 2.4 Speech Intelligence

    15. Unit 3: Techniques in Artificial Intelligence

    16. 3.1 Edge AI

    17. 3.2 Medical Imaging & Diagnostics

    18. 3.3 Autonomous Vehicle

    19. 3.4 Reinforcement Learning

    20. 3.5 Conversational AI

    21. 3.6 GAN, XAI, Synthetic Training Data

    22. Unit 4: Artificial Intelligence: Trends and Markets

    23. 4.1 AI Trends

    24. 4.2 AI Markets

    25. 4.3 AI in Sustainable Energy

    26. 4.4 AI in Financial Services

    27. 4.5 AI in Government

    28. 4.6 AI in Healthcare

    29. 4.7 IoT and AI in Agriculture

    30. Unit 5: Course Roadmap

    31. 5.1 Artificial Intelligence Course Roadmap

    32. 5.2 Category of Machine Learning Techniques

    33. Introduction to AI (Summary)

    34. Chapter 1: Introduction to Artificial Intelligence

    1. Unit 1: Introduction

    2. 1.1 Installing Anaconda for Python

    3. 1.2 Intro to Mathematics

    4. 1.3 Mathematical Symbols

    5. Unit 2: Basic Math for Data Science

    6. 2.1 Algebra

    7. 2.2 Sequence

    8. 2.3 Absolute Value and Euclidean Distance

    9. 2.4 Sets

    10. 2.5 Concept of Functions

    11. 2.6 Exponential and Logarithmic Functions

    12. 2.7 Natural Logarithms

    13. 2.8 Sigmoid Functions

    14. 2.9 Trigonometric Functions

    15. Unit 3: Understanding Data Science: Vector

    16. 3.1 Vector

    17. 3.2 Vector Norm

    18. 3.3 Inner Product

    19. 3.4 Orthogonal Condition

    20. 3.5 Normal Vector

    21. Cosine Similarity

    22. Unit 4: Understanding Data Science: Matrix

    23. 4.1 Calculating Matrix

    24. 4.2 Reverse Matrix

    25. 4.3 Linear Transformation

    26. 4.4 Eigenvalues and Eigenvectors

    27. Unit 5: Understanding Deep Learning: Derivatives

    28. 5.1 Limits

    29. 5.2 Differential Coefficient and Derivatives

    30. 5.3 Differential Method

    31. 5.4 Difference Between Logarithmic and Exponential

    32. 5.5 Derivatives of Composite Functions

    33. 5.6 High Order Derivatives and Partial Derivatives

    34. 5.7 Derivative of the Sigmoid Function

    35. Chapter 2: Math for Data Science (Slides)

    1. More lessons are coming soon

    1. More lessons are coming soon

    1. More lessons are coming soon

Artificial Intelligence

  • 3 Months
  • 240 Hours of training