Great Learning Courses

Back Propagation

Comprehensive exploration of backpropagation in neural networks, covering its principles, algorithm, types, advantages, and disadvantages for effective machine learning model training.

• YouTube
• 1 hour 40 minutes
• Self-Paced
• Free Video

Customer Relationship Management

Learn to enhance customer relationships, boost sales, and improve retention through effective CRM strategies, components, processes, and real-world examples.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

Graphs in Python

Learn graph concepts, types, operations, and traversal algorithms with Python implementation of BFS and DFS. Master essential graph theory for data structures and algorithms.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

Python Tkinter

Learn to create GUI applications in Python using Tkinter. Explore widgets, geometry management, and build interactive interfaces with labels, buttons, checkboxes, frames, and listboxes.

• YouTube
• 51 minutes
• Self-Paced
• Free Video

Chi Square Test

Learn to apply Chi-Square tests for categorical data analysis, including hypothesis testing, p-value interpretation, and implementation in Excel, Python, and R. Gain practical skills for statistical inference.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

Batch Normalization

Explore batch normalization in deep learning: its implementation, benefits, and impact on network performance. Learn when and how to use it effectively for improved model training.

• YouTube
• 1 hour 13 minutes
• Self-Paced
• Free Video

Search Algorithms in Java

Learn Java search algorithms: linear and binary search implementation, time and space complexity analysis, and algorithmic concepts for optimized data retrieval.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Selection Sort in Java

Learn to implement and analyze Selection Sort in Java, covering its logic, implementation, time complexity, and practical applications.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

ANOVA

Comprehensive introduction to Analysis of Variance (ANOVA), covering key concepts, types, and applications in statistical analysis. Includes practical examples and comparisons with other statistical tests.

• YouTube
• 1 hour 50 minutes
• Self-Paced
• Free Video

Data Structures and Algorithms in Python for Beginners

Comprehensive exploration of data structures and algorithms using Python, covering fundamental concepts, implementation, and analysis for beginners to enhance problem-solving skills.

• YouTube
• 7 hours 57 minutes
• Self-Paced
• Free Video

Random Forest Regression

Learn Random Forest Regression: its advantages, disadvantages, algorithm, and applications. Compare with logistic regression and explore its impact in machine learning across various domains.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

Prim's Algorithm in Java

Learn Prim's Algorithm for finding minimal spanning trees using Java, covering greedy programming concepts and practical implementation with detailed explanations and code examples.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

Java Data Structures

Master essential data structures in Java: arrays, stacks, queues, linked lists, trees, graphs, and hash tables. Gain practical implementation skills and understand their applications and trade-offs.

• YouTube
• 5 hours 26 minutes
• Self-Paced
• Free Video

Bias Variance Tradeoff

Explore the bias-variance tradeoff in machine learning, understanding its impact on model performance and learning strategies to balance these crucial factors for optimal predictions.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

BFS and DFS in Java

Explore BFS and DFS algorithms for graph traversal in Java, covering their concepts, implementation, and practical applications in computer science and programming.

• YouTube
• 56 minutes
• Self-Paced
• Free Video