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 courses.
The activities in each course will follow the structure below:
The pragmatic fact is that most real-world problems either don’t need deep learning or can be better dealt with shallower, simpler models by incorporating already well-understood domain knowledge. Deep learning requires a very large amount of labeled training data. However, with good feature engineering, combining the expert domain knowledge and the know-how of the internal machinery of the machine learning models, you can build solutions that work great. This approach has other benefits too: lesser labeled data, quicker turnaround time and more interpretable models. The Googles and Facebooks of the world, even with strong deep learning technologies available for them, routinely use classical machine learning methods for most problems. Classical machine learning is the workhorse of the real-world data-driven problem-solving.
Consider self-driving cars or Alexa kind of applications. They would mainly use deep learning for processing visual scenes or speech input, which makes up only a small, albeit a very important, part of the whole system. Much of the system would use rule-based, algorithmic and simpler ML models, reasoning with knowledge models, etc. to put together the whole act of self-driving or understanding and responding to the users. It’s these classical methods that make up the majority of the system. Such methods are even more important when you are working with tabular data, common in business problems.
The Hands-on Machine Learning Foundations Course is designed to provide you a strong foundation of the general concepts of machine learning while introducing you to some powerful classical machine learning methods, that would surely cover the majority of your real-world demands. Moreover, the concepts that you would master in this course will lay a perfect launchpad for more advanced models discussed in Advanced Course, which includes an introduction to deep learning.
The outline of the course is given below.
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
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.
The Hands-on Machine Learning Advanced Course will build on the ML Foundations and introduce you to some power tools of machine learning: Probabilistic Graphical Models, Neural Networks, and Deep Learning. With these techniques in your arsenal, you will be able to design solutions for problems involving making sense of visual data, understanding, and auto-responding to natural language queries/questions, knowledge representation and reasoning using the graphically modeled domain knowledge. We will cover deep learning models, including Convnets and RNNs, with emphasis on the practical process of designing and training models using Keras.
The outline of the Advanced Course is given below.
As far as Deep Learning is concerned, if mastering frameworks like Tensorflow or Caffe etc., was all it took to build production quality solutions, isn’t it unlikely that those frameworks would be open-sourced in the first place! Even if one has good training data, the key to building successful deep learning models lies in applying the theoretical knowledge to diagnose and tune them in a principled manner.
As of today, deep learning in practice is necessarily empirical and each iteration is time-consuming if not approached systematically. A good deep learning engineer would minimise costly iterations by following an appropriate design & train process that is backed by a thorough understanding of the underlying math machinery of the models.
To help you become a successful deep learning practitioner, we have designed this course to provide you the necessary theoretical knowledge and practical know-hows.
The detailed course outline is as under.
Ram is a mathematician practicing machine learning. I worked closely with Ram as part of SIGML (Special Interest Group for Machine Learning). I had worked at various machine learning projects at Google and had interacted with many ML experts. Ram is quite different from others in his depth of understanding. This makes his explanation so intuitive and easy to follow. His first principle approach makes us feel we are inventing the theory again, instead of just understanding or following it. Thanks Ram for spreading your valuable knowledge to fellow machine learning enthusiasts. ..more
If you are looking to become a consummate practitioner of Deep learning, understanding the Math is a necessary step. You look for a teacher who can find that balance between the practice and the Math and take you along. Ram is very passionate about teaching Math fundamentals and a hands-on practitioner. You will emerge from these sessions with an excellent foundation and confidence to play in this rapidly evolving space...more
When I met Ram in Oct 2016, I had completed 2 years of an MBA course with focus on stats and I was an independent data science consultant working with startups. I was a voracious learner and wouldn't miss any online course. While they all helped me in my journey, nothing has had the profound impact that learning machine learning through Ram did. In short, he has immense knowledge of the field, more importantly, the knowledge built on years of building practical and innovative solutions much before the hype that data science is receiving now. Ram, being an avid neuroscience enthusiast, has also helped to learn how to learn. I wish I had begun interacting with him when I began my journey in data science. He has taught me how to learn and apply the concepts to problems on the field. I wouldn't miss any opportunity to learn from him. ..more
Ramprakash is an artist, a mathematician, a machine-learning expert. Most of all, he is a true researcher. When he sets his sight on a problem to tackle, he brings in the full fire power of his creativity and deep knowledge of the state-of-the-art in the field. He had been researching and building machine learning systems for close to 15 years, before most people knew what machine learning meant. Teaching has been a personal passion of his, and this course brings his two passions together. Machine learning and teaching. ..more
Value for money. Most effective. Covers full breadth of ML not just Neural networks. Ram is awesome. He is one of the best teacher that i have ever listened to. He makes the basics clear and also explains the things in more structured and in layman terms. ..more
Ease and the simplicity of Ram in explaining the concepts. His in-depth practical and technical grasp on the subject.
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.