Computer Science Courses and Certifications

Introduction to Machine Learning and Algorithmic Bias

Explore AI and machine learning fundamentals while mastering algorithmic bias detection and mitigation strategies for responsible business applications.

• Coursera
• 6 hours 48 minutes
• Self-Paced
• Paid Course

Machine Learning and Data Analytics Part 1

Dive into data mining fundamentals, exploring preprocessing, classification, clustering, and regression techniques through hands-on projects and real-world case studies.

• Coursera
• 15 hours 20 minutes
• Self-Paced
• Paid Course

Machine Learning with Small Data Part 1

Discover modern deep learning techniques for training effective models with limited datasets, addressing challenges in healthcare, military, and industry applications.

• Coursera
• 14 hours 21 minutes
• Self-Paced
• Paid Course

Data Management for Analytics Part 2

Explore SQL fundamentals, NoSQL databases (MongoDB, Neo4j), and big data technologies to build essential data management skills for analytics and machine learning applications.

• Coursera
• 9 hours 40 minutes
• Self-Paced
• Paid Course

System Design Fundamentals

Discover Juval Löwy's revolutionary approach to system architecture using volatility-based decomposition instead of functional methods to create future-proof, maintainable software systems.

• Coursera
• 5 hours 35 minutes
• Self-Paced
• Paid Course

Statistical Learning for Engineering Part 1

Explore supervised and unsupervised machine learning algorithms, from linear regression to SVMs, with hands-on Python implementation for real-world engineering applications.

• Coursera
• 1 day 11 hours 38 minutes
• Self-Paced
• Paid Course

Machine Learning and Data Analytics Part 2

Explore advanced data mining techniques including clustering, neural networks, SVMs, and text mining with real-world applications across engineering sectors.

• Coursera
• 18 hours 25 minutes
• Self-Paced
• Paid Course

Coursera

ServiceNow Basics: IT Automation & AI-Powered Workflows

Master ServiceNow platform navigation, AI-driven workflow design, and automation tools like Predictive Intelligence and Virtual Agent to streamline IT operations and enhance service efficiency.

• Coursera
• 3 hours 37 minutes
• Self-Paced
• Paid Course

Birla Institute Of Technology And Science–Pilani (BITS–Pilani)

Data Structures and Algorithms

Master essential data structures like arrays, trees, and graphs, plus key algorithms including sorting, searching, and graph traversals for efficient problem-solving.

• Coursera
• 3 days 6 hours 46 minutes
• Self-Paced
• Paid Course

Web Design and User Experience Engineering Part 1

Discover HTML, CSS, and JavaScript fundamentals to build accessible, responsive web interfaces with semantic markup, modern layouts, animations, and interactive features.

• Coursera
• 2 days 2 hours 36 minutes
• Self-Paced
• Paid Course

Software Quality Control and Management Part 1

Discover software quality assurance fundamentals, testing methodologies, and automation tools like UFT and Selenium to build reliable software products.

• Coursera
• 20 hours 23 minutes
• Self-Paced
• Paid Course

Statistical Learning for Engineering Part 2

Explore advanced machine learning algorithms including neural networks, clustering, dimensionality reduction, and reinforcement learning with hands-on Python implementation.

• Coursera
• 1 day 46 minutes
• Self-Paced
• Paid Course

Comprehensive UX and Responsive Web Design

Master essential web development skills from HTML fundamentals to React, creating responsive and accessible websites while learning modern tools, best practices, and interactive UI/UX development techniques.

• Coursera
• 15 hours 41 minutes
• Self-Paced
• Paid Course

Data Management and Database Design Part 2

Master advanced database optimization, security, and cloud technologies while building production-level skills in transaction management, data governance, and disaster recovery strategies.

• Coursera
• 17 hours 19 minutes
• Self-Paced
• Paid Course

Program Structure and Algorithms Part 2

Master dynamic programming, network flow algorithms, computational complexity theory, Bayesian inference, and randomized algorithms for advanced problem-solving.

• Coursera
• 1 day 7 hours 42 minutes
• Self-Paced
• Paid Course