Are you looking for the best entry point to the field of Artificial Intelligence/Machine Learning (AI/ML)? You have come to the right place.


You have probably heard of AI/ML frameworks such as Tensorflow and PyTorch, and other platforms such as H20 and KNIME. The frameworks and platforms provide an incredible level of abstraction such that, with just a few lines of codes you can build powerful deep learning models.


However, understanding the fundamentals of AI/ML is essential to truly enjoy using the frameworks and to use them effectively. That’s where this course comes in. While there are many courses on AI, no other course brings this type of intuitive explanation to a vast set of complex concepts.


Upon completion, you will have an exceptionally clear and in-depth understanding of AI. You will be able to pick up a machine learning framework/platform of your choice and quickly become an outstanding developer in the field.


Enroll and start your 3-week journey (recommended pace: four hours per week, towards a total of 12 hours). 



Who Is This Course For?

Engineers/Developers who are relatively new to the field or who want to enter the field. 

Technology Managers and Executives who want to thoroughly understand AI and apply and deliver ideas in their businesses




Testimonials

Sebastian R

A very thorough and well designed course. Very adaptable to a wide variety of use-cases. The course clearly reflects Suresh's expertise in the field and the level of effort and thought he has put into designing this course.

Eva S

The content of this training is exceptional. A very easy to understand course illustrating specific examples of AI application. It covers a lot of information, yet it is very easy to absorb.

Alex O

Exceptional quality, right blend of theory and practical application! I found this course very actionable. I could easily relate to the lessons, and think of very specific ways to apply the learnings right away.

Greg H

As a developer, I loved it. The course makes it very easy to understand and try out coding. I am recommending this to all my friends.

Tatyana G

The content of this training is extremely relevant to someone like myself, who is looking for tangible examples of AI applications to a variety of real-life problems. The course is easy to understand, and is a great starting point for someone looking to expand their analytical arsenal. It covered a lot of ground, and helped generate transformational ideas in my day-to-day work.

Course Curriculum

  • 1
    Course Introduction
    • Welcome To Demystifying AI [1 min]
    • Setting Context: AI Around Us [13 min]
  • 2
    What Is AI/ML And How It Works
    • Computer Programs [10 min]
    • House Price Prediction [14 min]
    • Machine Learning Definition [8 min]
    • Sidebar: Model Vs Algorithm [2 min]
    • How ML Works [10 min]
  • 3
    Definitions: ML, AI, Data Science
    • What is AI
    • Data Science Definition
    • Data Science & Questions
    • Data Science Types
  • 4
    Classification Models
    • Prediction Types [14 min]
    • Motivating Example [4 min]
    • Outputting Probabilities [8 min]
    • Classification Model Training [14 min]
  • 5
    Neural Networks And Deep Learning
    • Sidebar: About Pixels [9 min]
    • Neural Networks Introduction [12 min]
    • Neural Networks Definitions [9 min]
    • Activations [5 min]
    • Deep Learning [6 min]
    • Sidebar: Loss, Cost, Entropy [3 min]
    • Neural Network Training [7 min]
    • Why the Word 'Neural' [7 min]
  • 6
    Why Deep Learning (DL)
    • Why Deep Learning [10 min]
    • Why Deep Learning: Representation Learning [14 min]
    • Why Deep Learning: Transfer Learning [5 min]
    • Deep Learning Challenges [5 min]
  • 7
    Machine Learning Process
    • Machine Learning Process: From Idea To Deployment [3 min]
    • Define Problem/Opportunity [4 min]
    • Find Data [11 min]
    • Process Data: Data Hygiene [12 min]
    • Process Data: Missing Data [13 min]
    • Process Data: Numeric Conversions [10 min]
    • Process Data: Feature Engineering [4 min]
    • Process Data: Feature Scaling [6 min]
    • Why Evaluation Plan [3 min]
    • Evaluation Challenges [14 min]
    • Underfitting [7 min]
    • Overfitting Identification [8 min]
    • Overfitting Remediation [8 min]
    • Evaluation: Putting It Together [5 min]
    • Learning Types [12 min]
    • Select Model [6 min]
    • Create Model [11 min]
    • Deploy Model [7 min]
  • 8
    Naive Bayes Classifier
    • Probabilities Primer For Machine Learning [9 min]
    • Conditional Probabilities [14 min]
    • Naive Bayes Introduction [9 min]
    • Naive Bayes Example [10 min]
  • 9
    Decision Trees
    • Decision Trees [10 min]
    • Decision Trees - Motivating Example [8 min]
    • Decision Trees - Optimization Metric [4 min]
    • Decision Tree Algorithm [17 min]
    • Greedy Algorithm [8 min]
    • Decision Trees: Pros and Cons [8 min]
  • 10
    Ensemble Learning
    • Ensemble Learning Motivation [11 min]
    • Random Forest [14 min]
    • Why Random Forest [4 min]
    • Ensemble Learning: Closing Notes [10 min]
  • 11
    Convolutional Neural Network
    • Convolutional Neural Network Problem Setup [4 min]
    • Filters [12 min]
    • On Visualization [6 min]
    • Convolutional Neural Network: Layers [12 min]
    • Layers And View Size [5 min]
    • Putting Convolutional Neural Network Together [7 min]
    • Inspiration For Convolutional Neural Network [6 min]
  • 12
    Optional: Additional CNN Topics
    • Optional: Strides [7 min]
    • Optional: Padding [8 min]
    • Optional: Pooling [9 min]
  • 13
    Emotion Recognition
    • Why Emotion Recognition [7 min]
    • Emotion Recognition Setup [7 min]
    • Multi-Task Classifier [8 min]
    • Multi-Class Classifier [7 min]
  • 14
    Reinforcement Learning
    • Reinforcement Learning Introduction [15 min]
    • How Reinforcement Learning Works: Setup [3 min]
    • How Reinforcement Learning Works: Policy Gradient [15 min]
    • Reinforcement Learning Challenges [7 min]
  • 15
    Optional: Gradient Descent Walk Through
    • Optional: Gradient Descent Setup [7 min]
    • Optional: Putting Together Gradient Descent [20 min]
    • Optional: Gradient Descent Closing Thoughts [7 min]
  • 16
    Optional Neural Network Topics
    • Neural Network Model Algebra [4 min]
    • Backpropagation [4 min]
  • 17
    Closing Note
    • Thank You! [1 min]
  • 18
    Citations And References
    • Consolidated Citations

Course Guidelines

  • Recommended Equipment:

    Most lessons use data tables and free-form writing. For the best viewing experience, please use computers or tablets.

  • Follow The Sequence:

    Some lessons make reference to previous videos. For best continuity, please follow the course in the prescribed sequence.

  • About Optional Videos:

    These videos provide additional quantitative intuition on some topics. They may not be of interest to all audiences and can be skipped without any loss of continuity.

What This Course Is Not:

Not a programming course: 

This is not a hands-on, coding course. There are many incredible alternatives available for that. Based on your preference of programming language and technology stack, try the online programming tutorials & examples provided by the vendors or open-source portals. Almost all of them have exceptional, free hands-on programming tutorials that you can leverage. 

Not a discussion on opinions:  

I will not be discussing opinions such as, is AI for the greater good; will AI take away jobs; what is ethical and what is not etc. But this course will make you an expert to the level that you can publish your own opinions on such topics. 



Meet Your Instructor

Suresh Kumar, AI/ML Architect

If I were to describe myself in one phrase,  TLC is my thing. Yes, Tender Loving Care too, but in this case, I am referring to Teaching - Learning - Coding. 

Teaching: My passion and ability for this will be evident when you go through my course. Teaching an introductory Computer Science course to non-computer science majors at the University of New Hampshire (UNH) shaped by teaching skills. I was one of the highest rated teachers (rated by students) during my time at UNH. 

Learning: I learn, therefore I am. But if credentials are a more objective measure of my learning prowess, I have an MBA from Stern School of Business, NYU; a Masters in Computer Science from the University of New Hampshire; a Bachelor’s in Engineering from Delhi University; and I am a Google Cloud certified Data Engineer. 

Coding: Coding is my meditation. Daily coding is a must for me. I started my professional career with coding, but even as my roles changed back and forth over the years, I always maintained my coding edge. Today, Python is my language of choice - well, may be with a side of SQL and JavaScript. 

I have enjoyed being an engineer, data engineer, AI consultant, architect (the tech kind), entrepreneur, and at times Financial Services domain expert, at companies such as Accenture, Atos Syntel, E*Trade, Goldman Sachs, JP Morgan, and NY Life, to name a few. I am currently the Head of AI Solutions for PARC, a Xerox company.

So why did I create this course? In my numerous interactions with people across industries and roles, I noticed an enormous gap in the deeper understanding of AI. I believe that I am uniquely positioned to help bridge that gap. I know you will agree with me as soon as you start the course!