BIGDataAIHub
The resources below have been curated by IMI BigDataAIHub. They include free and free-to-audit courses, certifications, tutorials, and learning materials.
Developing AI Applications
Build practical AI applications using Python through this 10-part Skill Track, which combines guided courses and hands-on projects covering data preparation, model development, evaluation, and deployment for real-world use.
Python Data Science Handbook
A guide for data analysis using Python, covering essential libraries such as IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn, and offering practical guidance through Jupyter notebooks.
Python for Data Analysis
A guide to using Python for data analysis, covering topics such as data wrangling with pandas, numerical computing with NumPy, and data visualization with matplotlib.
Difference Between Big Data and Data Science
The article explains how Big Data involves the management and processing of vast datasets, while Data Science focuses on analyzing these datasets to extract meaningful insights and inform decision-making.
A Mind Map of Core Machine Learning Concepts
This blog post provides a visual mind map outlining core machine learning concepts, including supervised, unsupervised, and reinforcement learning. It serves as a beginner-friendly roadmap that connects key topics, methods, and tools like scikit-learn, TensorFlow, and PyTorch.
Machine Learning Mastery
Learn practical machine learning with Python and scikit-learn, progressing from basics to advanced topics through modules tailored to your interests.
Python Pandas Tutorial (Part 2): DataFrame and Series Basics - Selecting Rows and Columns
A video from Corey Schafer that provides an introduction to the fundamental data structures in the Pandas library
Python and Pandas Fundamentals
A video from UTM faculty teaching Python basics, including syntax, data types, control structures, and functions
Pandas Advanced Data
A video from UTM faculty introducing variables and basic Python programming concepts.