There is no substitute for experience. And that holds true in the machine learning industry as well. Learning the ins and outs of machine learning takes time, effort, dedication, a whole lot of learning, and accumulated experience.
This course is an amalgamation of various talks by machine learning experts, practitioners, professionals and leaders who have decades upon decades of learning experience with them. They have already gone through the entire learning process and they showcase their work and thought process in these talks.
This course features rockstar data science experts like Sudalai Rajkumar (SRK), Professor Balaraman Ravindran, Dipanjan Sarkar, Kiran R and many more!
From building an end-to-end credit risk model to evaluating your model for bias, there is a LOT to learn from the Expert Talks course so get started today!
- About the "Expert Talks" Course
- Framework to Manage End to End ML Projects by Kiran R
- Patterns of Insights - By Anand S
- Evaluating ML Models for Bias – Build an Interpretable Model using a Financial Dataset by Rajesh Jeyapaul & Prateek Goyal
- Building an End-to-End Credit Risk Model by Arihant Jain
- Hyperparameter optimization using Bayesian Optimization by Abhishek Periwal
- Performing Feature Selection in High Dimensional Spaces - By Sidharth Kumar
- Introduction to the World of Reinforcement Learning: Applications and Challenges - By Professor Balaraman Ravindran
- State of Transfer Learning in NLP: BERT vs GPT2 vs XLNet by Sudalai Raj Kumar (SRK)
- Effective Feature Engineering – A Structured Approach to Building Better ML Models -By Dipanjan Sarkar
- Automating the Machine Learning Pipeline with AutoML -By Dr. Sunil Kumar Chinnamgari
- Panel Discussion Keeping Relevant in the World of Artificial Intelligence -By Speaker Panel
Here’s a summary of each Expert Talk in the course:
Framework to Manage End-to-End Machine Learning Projects by Kiran R
Learn how to manage machine learning projects from the concept stage to the final completion stage. Various aspects of a typical machine learning project, including how to successfully manage one from scratch will be discussed along with tips on model building and last mile optimization.
Patterns of Insights by Anand S
Once you’ve got the data, the next questions to ask are:
● How do you get interesting stories out of this data?
● And how do you narrate these stories?
The first step here is to figure out what the interesting insight is. A fair bit of this is understanding the domain well enough, and people tend to miss out the soft skills aspect. The second step involves converting these insights into a series of questions that the data can answer.
Are there patterns of questions that we can pose to the data and is there a systematic and structured way by which we can explore it? This talk by Anand S will answer these questions and more.
Evaluating Machine Learning Models for Bias by Rajesh Jeyapaul and Prateek Goyal
Although machine learning, by its very nature, is always a form of statistical discrimination, the discrimination becomes objectionable when it places certain privileged groups at a systematic advantage and certain unprivileged groups at a systematic disadvantage. In this hack session, Rajesh and Prateek will discuss the concepts and capabilities of a model to test for biases and explanations along with the wider spectrum of explainability methods, notably data explanations, metrics and persona specific explanations.
Building an End-to-End Credit Risk Model by Arihant Jain
This talk is about developing a Machine Learning model that determines which loan applicants are credible and developing a monitoring framework and feedback loop for the Credit Risk Model.
Hyperparameter Optimization using Bayesian Optimization by Abhishek Periwal
Hyperparameter tuning is an essential part of any machine learning pipeline. With better compute we now have the power to explore more range of hyperparameters quickly but especially for more complex algorithms, the space for hyperparameters remains vast and techniques such as Bayesian Optimization might help in making the tuning process faster. Witness how you can use HyperOpt library uses Bayesian techniques to find the best hyperparameters with a real dataset.
Performing Feature Selection in High-Dimensional Spaces by Sidharth Kumar
Typical feature reduction algorithms suffer from problems relating to class of features (categorical, continuous) and the complexity resulting from the number of features, thus making redundant feature elimination a difficult task. Additionally, it is not clear as to how the objective function affects the feature selection. In this hack session, Sidharth will discuss how to use moving set of thresholds for feature selection, which are optimized based on a Markov Chain Monte Carlo that adjusts the thresholds based on the final model fitness.
Introduction to the World of Reinforcement Learning by Professor Balaraman Ravindran
In this talk, Professor B. Ravindran will introduce you to the crux of what reinforcement learning is and why it’s a distinct learning paradigm as compared to the traditional machine learning approaches. He will also cover the different kinds of applications where reinforcement learning can be applied, and the challenges industries are facing in applying RL.
State of Transfer Learning in NLP by Sudalai Rajkumar (SRK)
NLP's latest pre-trained language models like BERT, GPT2, TransformerXL, XLM, etc. are achieving state of the art results in a wide range of NLP tasks. In this hack session, community favourite and Kaggle Grandmaster SRK will compare the performance of these different pre-trained models along with pre-trained word vector models on classification tasks.
Effective Feature Engineering for Building Better ML Models by Dipanjan Sarkar
In this hack session, Dipanjan will be taking a structured and comprehensive hands-on approach (with Python) to feature engineering, where we will explore two interesting case studies based on real-world problems!
Automating the Machine Learning Pipeline by Dr. Sunil Kumar Chhinnamgari
This conference talk attempts to introduce the concept of AutoML using two real-world case studies.
Do I need to install any software as part of the course?
No, you don’t need any software for this course. A working internet connection is enough to get you started!
Do I need to take the modules in a specific order?
No, you can pick and choose the talks you want to listen to in any order you prefer.
How long can I access the course?
Address common questions ahead of time to save yourself an email.