Applied Course Archives

Text Data Mining, Cleansing, Treating, Structural Representation, and Visualization

This course explains all the necessary concepts and skills required to mine, explore, cleanse, and get the highest quality of information from unstruc

Difficulty: Advanced
Duration: Approximately 7 hours

Applied Course

Sentiment Analysis – Unstructured Text Data

This course introduces the fundamental concepts of text analytics and applies different algorithm to determine the sentiment of a given text.

Difficulty: Beginner
Duration: Approximately 3.5 hours

Applied Course

Introduction to Neural Networks

This course introduces the basic principles and working of neural networks based on which the learner will be able to build some of the most complex n

Difficulty: Advanced
Duration: Approximately 8 hours

Applied Course

Text Analytics – Classification and Clustering

This course focuses on the concepts required to apply clustering and classification algorithms to text data along with hands-on exposure to model va

Difficulty: Advanced
Duration: Approximately 5 weeks

Applied Course

Optimization Techniques – Specialized Optimization Problems

This course focuses on the vital importance of several specialized optimization problems such as transportation problems, knapsack problems and travel

Difficulty: Intermediate
Duration: Approximately 4 hours

Applied Course

Optimization Techniques – Network Flow Problems

This course introduces network flow problems, which is a particular type of optimization problem and explores the concepts of Shortest Path Problem an

Difficulty: Intermediate
Duration: Approximately 4 hours

Applied Course

Introduction to Optimization Techniques

This course is designed to guide learners through various stages of solving an optimization problem and focuses on providing exposure to building a ta

Difficulty: Intermediate
Duration: Approximately 4 hours

Applied Course

Fundamentals of Time Series Analysis

This course introduces the learner to the concept of time series data and focuses on various visual and statistical techniques to analyse and infer

Difficulty: Advanced
Duration: Approximately 6 hours

Applied Course

Hyperparameter Tuning in Tree-Based Models

This course is designed to help the learners understand various hyperparameters used in tree-based models such as decision tree to solve regression

Difficulty: Advanced
Duration: Approximately 12 hours

Applied Course

Introduction to Principal Component Analysis (PCA)

This course will provide a comprehensive understanding of the PCA technique, covering both its theoretical concepts and practical implementation in re

Difficulty: Intermediate
Duration: Approximately 8.5 hours

Applied Course

Understanding Decision Trees

This course will provide learners an in-depth understanding of decision tree algorithm and its practical implementation in a real-world based business

Difficulty: Intermediate
Duration: Approximately 8.5 hours

Applied Course

Hyperparameter Tuning in Support Vector Machines

This course is designed to help the learners understand various hyperparameters used in the Support vector machine for regression and classification

Difficulty: Advanced
Duration Approximately 12 hours

Applied Course

Basics of Hyperparameter Tuning

This course will allow learners to understand the concept of hyperparameter tuning for both regression and classification models that can help in impr

Difficulty: Advanced
Duration: Approximately 12 hours

Applied Course

Introduction to Support Vector Machines

This course is designed to help the learners start from the very foundation of the SVM algorithm and quickly become a professional in implementing t

Difficulty: Intermediate
Duration: Approximately 8 hours

Applied Course

Machine Learning – Segmentation

This course is designed to provide a comprehensive understanding of Linear Regression Models. Learners will explore aspects of model building and pr

Difficulty: Intermediate
Duration: Approximately 8 hours

Applied Course

Introduction to Naive Bayes Classifiers

This course is curated to introduce the learner to the underlying aspects of the Naive Bayes algorithm and apply the algorithm in a real-world based b

Difficulty: Intermediate
Duration: Approximately 8.5 hours

SOLUTIONS OVERVIEW

Pricing Options

SOLUTIONS OVERVIEW

Pricing Options

Courses

Text Data Mining, Cleansing, Treating, Structural Representation, and Visualization

Sentiment Analysis – Unstructured Text Data

Introduction to Neural Networks

Text Analytics – Classification and Clustering

Optimization Techniques – Specialized Optimization Problems

Optimization Techniques – Network Flow Problems

Introduction to Optimization Techniques

Fundamentals of Time Series Analysis

Hyperparameter Tuning in Tree-Based Models

Introduction to Principal Component Analysis (PCA)

Understanding Decision Trees

Hyperparameter Tuning in Support Vector Machines

Basics of Hyperparameter Tuning

Introduction to Support Vector Machines

Machine Learning – Segmentation

Introduction to Naive Bayes Classifiers

Welcome Back!

Forgot password

Reset Password requested

Sign Up and Start Learning