Courses

Time Series Forecasting

This course introduces the concepts of Time Series Forecasting and how to prepare data for the concepts to be applied on real-world business scenarios

Difficulty: Advanced
Duration: Approximately 4 hours to complete

Crash Course

Fundamentals of Data Pre-processing

This course introduces the basics of data preprocessing and exploratory data analysis used for arriving at valuable insights for decision making.

Difficulty: Beginner
Duration: Approximately 1 hour

Crash Course

Machine Learning – Regularization in Linear Models

This course explains different regularization techniques in linear machine learning algorithms and how those could be used to treat the problem of ove

Difficulty: Intermediate
Duration: Approximately 3 hours

Crash Course

Introduction to Natural Language Processing (NLP)

This course introduces the concepts of Natural Language Processing, which creates systems such as speech recognition, machine translation, spam detect

Difficulty: Advanced
Duration: Approximately 5 hours

Crash Course

Introduction to Explainable AI (XAI) using SHAP

This course introduces the concept of SHAP methodology, an XAI technique, which can be applied to understand the results an ML model and how they can

Difficulty: Advanced
Duration: Approximately 5 hours

Crash Course

Introduction to Explainable AI (XAI) for Text Data using LIME & SHAP

This course explains the need of Explainable AI (XAI) for text data and introduces the concepts of LIME & SHAP, which are the techniques of XAI.

Difficulty: Advanced
Duration: Approximately 5 hours

Crash Course

Introduction to Explainable AI (XAI) using LIME

This course introduces the concept of LIME methodology, an XAI technique, which can be applied to understand the results an ML model and how they can

Difficulty: Advanced
Duration: Approximately 5 hours

Crash Course

Introduction to Gradient Boosting

This course introduces the concepts of Gradient Boosting Classifier and how it can be applied to achieve better accuracy & predictions for the problem

Difficulty: Advanced
Duration: Approximately 5 hours

Crash Course

Introduction to XGBoost

This course introduces the concept of Extreme Gradient Boosting Classifier that clearly lays out improvements of the algorithm over other Boosting met

Difficulty: Advanced
Duration: Approximately 5 hours

Crash Course

Introduction to CatBoost

This course introduces the concept of Catboost algorithm, that can easily integrate with different deep learning frameworks and can work on diverse da

Difficulty: Advanced
Duration: Approximately 5 hours

Crash Course

Introduction to AdaBoost

This course introduces the concept and implementation of Adaptive Boosting (AdaBoost) for both Classification and Regression tasks.

Difficulty: Advanced
Duration: Approximately 5 hours

Crash Course

Model Evaluation Techniques – Classification Models

This course introduces the different performance measures to evaluate the performance of Classification models.

Difficulty: Intermediate
Duration: Approximately 3.5 hours

Crash Course

Fundamentals of Data Visualization – Part I

Get started with this applied course focusing on the building blocks of analytics.

Difficulty: Beginner
Duration: Approximately 3 hours

Crash Course

Model Selection Techniques

This course introduces the various techniques used for selecting a better predictive model amongst the different models built for the problem in hand.

Difficulty: Intermediate
Duration: Approximately 3.5 hours

Crash Course

Machine Learning – Bagging and Random Forest

This course introduces Ensemble techniques and apply the concepts of Bagging and Random Forest to find solutions to real-world problems. and predictab

Difficulty: Advanced
Duration: Approximately 5 hours

Crash Course

Data Transformation

This course introduces the concept of Data transformations to convert raw data into analytics ready data that can help in data summarization.

Difficulty: Beginner
Duration: Approximately 2.5 hours

SOLUTIONS OVERVIEW

Pricing Options

SOLUTIONS OVERVIEW

Pricing Options

Time Series Forecasting

Fundamentals of Data Pre-processing

Machine Learning – Regularization in Linear Models

Introduction to Natural Language Processing (NLP)

Introduction to Explainable AI (XAI) using SHAP

Introduction to Explainable AI (XAI) for Text Data using LIME & SHAP

Introduction to Explainable AI (XAI) using LIME

Introduction to Gradient Boosting

Introduction to XGBoost

Introduction to CatBoost

Introduction to AdaBoost

Model Evaluation Techniques – Classification Models

Fundamentals of Data Visualization – Part I

Model Selection Techniques

Machine Learning – Bagging and Random Forest

Data Transformation

Welcome Back!

Forgot password

Reset Password requested

Sign Up and Start Learning