We are not going to just teach you machine learning. We are going to provide you a comprehensive platform to facilitate the learning of both theory and applications of machine learning through your active participation. We will equip you with knowledge and skills necessary to tackle the real-world problems in a principled manner. In essence, we aim to save you from the failures and frustrations of "spray and pray" or "blackbox" methods of unskilled practitioners.

We have custom designed learning modules for concepts with techniques that are best suited for each of them. While some concepts are most efficiently learnt through video lectures, some others are understood better via active explorations. Real-world problem-solving contexts could be apt for yet others. The mix of these strategies along with pedagogical techniques like spaced-repetition, reflection, in-line quizzing etc., are incorporated naturally into the course.

Course Details

Hands-on Machine Learning - Foundations

This is a one-month course, with video lectures to enable self-paced learning, simulators and interactive modules to explore concepts, in-person sessions for tutorials and deeper discussions, and assistance via scheduled mid-week Google Hangout sessions with the instructor for hands-on projects.

Week 1

  • Introduction to ML
  • Problems Addressed by ML - Supervised, Unsupervised, Reinforcement Learning
  • General Framework of ML
  • Evaluating ML Algorithms (train, test, Cross-validation)
  • Linear Regression (Visual Introduction)
  • Logistic Regression (Visual Introduction)
  • K-Means Clustering (Visual Introduction)
  • Feature Vectors - Feature encoding, embedding, dimensionality reduction

Week 2

  • Decision Trees & Random Forests
  • Supervised Learning
  • Unsupervised Learning
  • Regularisation, Bagging, Boosting

Week 3

  • Support Vector Machines
  • Dealing with non-linearity
  • Hyper-parameter tuning & Regularisation

Week 4

  • Linear Regression
  • Logistic Regression
  • Softmax Regression
  • K-Means Clustering
  • Agglomerative Clustering
Instructor Profile

Ram Prakash H.

Course Designer & Instructor

These courses are designed is designed and delivered by Mr. Ram Prakash H. Ram holds a B.Tech degree in Computer Science from IIT Madras. He has been an entrepreneur, Machine Learning researcher, and a hands-on practitioner for more than 15 years and is currently working with Flipkart as ML/Data Science Consultant in Bangalore. He has built and shipped several ML based technologies like

  • Innovation of Quillpad, first of its kind ML based multilingual predictive transliteration engine for Indian languages. Quillpad has been instrumental in triggering the rapid rise of user generated content in Indian languages on the internet. For this pioneering work, he was recognised among top 20 innovators in India by MIT TR35 awards.
  • Co-founding and leading the R&D team of a company which developed deep learning models for understanding aesthetic elements of fashion products.
  • Designing and developing an end-to-end industry grade machine learning product for converting scanned multilingual books to editable e-pubs, with state-of-the-art OCR accuracy for Indian languages. This technology was acquired by a leading e-book publisher in India.
Apart from MIT TR35 recognition, he has been,
  • invited as a Speaker at the top-tier International Conference on Computational Linguistics and
  • a winner of Nokia Best India Innovation Award

As a self-taught ML practitioner, he understands the questions faced by uninitiated learners and the dangers of learning through the black-box approach. His workshops will enable efficient learning for participants, through explanation of underlying principles to make the functioning of said methods more transparent and easy to understand.

On the research front, he is working on creating an AI-assisted active learning environment to help learners master a wide range of subjects.

Ram is an avid Runner and Badminton player. Connecting the dots through the application of Science and Math in any activity that he pursues is what gives him an edge over others in terms of the time taken to learn and master the techniques.His unconventional teaching style comprises anecdotes from all fields and makes his courses an enriching experience for learners.


Course Fee: Rs. 35,000/-

Why think twice? We offer a "No Question Asked Refund" in case you feel, after the first session, that this course is not helping you.



  • No prior knowledge of Machine Learning required
  • Should be comfortable coding in Python
  • Elementary knowledge of matrices, probability and differential calculus is preferred

Venue and other details

Venue: IKP EDEN, Koramangala, Bengaluru, Karnataka 560029.

Starting Date: 9th September, 2018

Timings: Sundays 9:00AM to 5PM (9th, 16th, 23rd, 30th September)

Week 1 (Probabilistic Graphical Models)

  • Naive Bayes Models
  • Bayesian Networks
  • Gaussian Mixture Models

Week 2 (Probabilistic Models for Spatial & Temporal data)

  • Markov Random Fields
  • Hidden Markov Models
  • Conditional Random Fields

Week 3 (Neural Networks)

  • Neurons
  • Understanding the need for Bias, Multiple Layers, and Non-linearity
  • Single Layer Neural Network ( Gradient Descent )
  • Multi-layer Neural Network ( Backpropagation )
  • Training & Tuning Neural Networks (Hyper-parameter tuning)

Week 4 (Deep Learning)

  • Deep Learning Introduction
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Using Pre-trained Models (transfer learning)

Course Fee: Rs. 45,000/-

Why think twice? We offer a "No Question Asked Refund" in case you feel, after the first session, that this course is not helping you.

Venue and other details

Venue: IKP EDEN, Koramangala, Bengaluru, Karnataka 560029.

Starting Date: 11th November, 2018

Timings: Sundays 9:00 am to 5:00 pm (Nov 11th and 18th, Dec 2nd and 9th)

Features of
Active learning
  • Increased engagement
  • Sparking Creativity
  • Deepening understanding
  • Widening participation
Benefits of
In-person Sessions
  • Better focus on learning and less distraction
  • Individualized and personalized support for students
  • Enhancement of learning by a classroom discussion
  • Enforcement of real-time discipline and structure
Advantages of
Guided Practice
  • Decontextualise learning from classroom to "real life" scenario
  • Scaffolding of the learner's attempt through support, encouragement, hints and feedback
  • Gradual transition of cognitive skills from modelling stage to independent practice
  • More confidence to apply the skills independently


About US

AI is here to stay. It will change the type of jobs humans have to do. We believe that the current educational system, as well as the professional skill development programs, are not designed for equipping people for this imminent scenario.

Our team is working on scalable learning environments to help people learn various subjects in such a way that empowers them to do tasks which AI will not be able to in the next decade. Such skills require deeper conceptual understanding, ability to formulate and solve problems and analyse and troubleshoot unseen situations. We believe that AI is going to play a significant role in achieving it. Our research focuses on addressing education-related problems by using latest advances in AI, cognitive sciences, and gamification. In essence, we are embracing AI to help ourselves stay a step ahead.