Curriculum

20 Chapters · 20 Lessons

  1. 1

    Book Preview

    1. Kickstart Artificial Intelligence Fundamentals Free preview
  2. 2

    Introduction

    1. (Included in full purchase)

      Introduction

  3. 3

    Chapter 1 : Introduction and Evolution of AI Technologies

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      Introduction and Evolution of AI Technologies

  4. 4

    Chapter 2 : Modern Approach to AI

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      Modern Approach to AI

  5. 5

    Chapter 3 : Introduction to Machine Learning

    1. (Included in full purchase)

      Introduction to Machine Learning

  6. 6

    Chapter 4 : Regression Versus Classification Model

    1. (Included in full purchase)

      Regression Versus Classification Model

  7. 7

    Chapter 5 : Naive Bayes as a Linear Classifier

    1. (Included in full purchase)

      Naive Bayes as a Linear Classifier

  8. 8

    Chapter 6 : Tree-Based Machine Learning Models

    1. (Included in full purchase)

      Tree-Based Machine Learning Models

  9. 9

    Chapter 7 : Distance-Based Machine Learning Models

    1. (Included in full purchase)

      Distance-Based Machine Learning Models

  10. 10

    Chapter 8 : Support Vector Machines

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      Support Vector Machines

  11. 11

    Chapter 9 : Introduction to Artificial Neural Networks

    1. (Included in full purchase)

      Introduction to Artificial Neural Networks

  12. 12

    Chapter 10 : Training Neural Networks

    1. (Included in full purchase)

      Training Neural Networks

  13. 13

    Chapter 11 : Introduction to Convolutional Neural Networks

    1. (Included in full purchase)

      Introduction to Convolutional Neural Networks

  14. 14

    Chapter 12 : Classification Using CNN

    1. (Included in full purchase)

      Classification Using CNN

  15. 15

    Chapter 13 : Pre-Trained CNN Architectures

    1. (Included in full purchase)

      Pre-Trained CNN Architectures

  16. 16

    Chapter 14 : Introduction to Recurrent Neural Networks

    1. (Included in full purchase)

      Introduction to Recurrent Neural Networks

  17. 17

    Chapter 15 : Introduction to Long Short-Term Memory (LSTM)

    1. (Included in full purchase)

      Introduction to Long Short-Term Memory (LSTM)

  18. 18

    Chapter 16 : Application of LSTM in NLP and TS Forecasting

    1. (Included in full purchase)

      Application of LSTM in NLP and TS Forecasting

  19. 19

    Chapter 17 : Emerging Trends and Ethical Considerations in AI

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      Emerging Trends and Ethical Considerations in AI

  20. 20

    INDEX

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      INDEX

About the course

AI is transforming industries, driving innovation, and shaping the future of technology. A strong foundation in AI fundamentals is essential for anyone looking to stay ahead in this rapidly evolving field. Kickstart Artificial Intelligence Fundamentals is a comprehensive companion designed to demystify core AI concepts, covering Machine Learning, Deep Learning, and Neural Networks. Tailored for all AI enthusiasts, this book provides hands-on Python implementation using the TensorFlow-Keras framework, ensuring a seamless learning experience from theory to practice. Bridging the gap between concepts and real-world applications, this book offers intuitive explanations, mathematical foundations, and practical use cases. Readers will explore supervised and unsupervised Machine Learning models, master Convolutional Neural Networks for image classification, and leverage Long Short-Term Memory networks for time-series forecasting. Each chapter includes coding examples and guided exercises, making it an invaluable resource for both beginners and advanced learners. Beyond technical expertise, this book explores emerging trends like Generative AI and ethical considerations in AI, preparing readers for the challenges and opportunities in the field. This book will provide you the essential knowledge and hands-on experience to stay competitive. Don’t get left behind—embrace AI and future-proof your career today!

About the Author

Dr. S. Mahesh Anand is a distinguished educator, corporate trainer, keynote speaker, and consultant specializing in data science, machine learning, and artificial intelligence. With over two decades of experience, Dr. Anand has been instrumental in shaping the learning journey of more than 50,000 students and professionals across India. Dr. Anand served as a full-time faculty member at VIT University (Vellore) for a decade, where he honed his academic and research skills. In 2012, he founded his consulting and training firm, Scientific Computing Solutions (SCS-India).