Certifications Guide: BIGDataAIHub

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.

Teaches how to build AI tools and write effective prompts for AI systems.

Teaches how to build AI tools and write effective prompts for AI systems.

Modules on cloud computing, AI, and data science for university students to gain hands-on experience.

Introduces AI concepts, explores data center and cloud infrastructures, and prepares learners for the NVIDIA-Certified Associate certification.

A video by Simplilearn breaking down and distinguishing artificial intelligence, machine learning, deep learning, and data science.

A workshop covering foundational tools, libraries, and techniques for AI/graphics development, delivered over multiple sessions.

Learn about the ethical challenges of AI and big data through case studies, NLP applications, and examining their impact on society.

A six-session workshop that teaches foundational techniques and tools for AI/training workflows, aimed at intermediate learners.

A six-session course covering advanced AI/graphics tools and techniques.

Teaches effective prompt creation to get better results from ChatGPT for coding, writing, and problem solving.

Teaches Python programming fundamentals, including functions, conditionals, loops, object-oriented programming, and file handling, through hands-on projects.

Teaches how to clean, analyze, aggregate, and visualize tabular data using Python’s Pandas library.

Teaches how to import, clean, and analyze data using pandas DataFrames, covering tasks like filtering, aggregating, and visualizing real-world datasets.

MiniTorch is an open-source educational framework for learning deep learning concepts by building neural networks from scratch in Python.

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.

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 video from IMI/Parsa Vafaie covering how to prepare data (feature engineering) and contrasting supervised vs. unsupervised machine learning methods.

Learn advanced tools and techniques used in AI and graphics development with NVIDIA’s software and frameworks.

A 4-hour course designed to enhance your Python skills by teaching data visualization with Matplotlib and data manipulation using pandas DataFrames.

Teaches you how to use SQL to store, query, and manipulate data in relational databases.

Covers networking fundamentals, including the OSI model, TCP/IP protocols, Ethernet technology, and data center requirements.

A course that introduces Python syntax, functions, conditionals, loops, and data structures like lists and dictionaries, with hands-on exercises tailored for data science applications.

A course that covers tabular data manipulation and analysis using Python's Pandas library, covering data ingestion, cleaning, aggregation, and integration with Scipy and Matplotlib.

Introduces Python fundamentals like variables, loops, functions, and data structures through structured lessons and practical exercises.

Teaches how to fine-tune, deploy, and manage large language models using open-source tools and best practices.

Learn practical machine learning with Python and scikit-learn, progressing from basics to advanced topics through modules tailored to your interests.

A Class Central video teaching Kaggle Grandmaster strategies for building high-performing recommendation systems.

A video from UTM faculty introducing variables and basic Python programming concepts.

Learn to create effective prompts for generative AI tools like ChatGPT to enhance applications in business, education, and everyday tasks.

A video from UTM faculty teaching Python basics, including syntax, data types, control structures, and functions

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.

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.

A video from Corey Schafer that provides an introduction to the fundamental data structures in the Pandas library

Teaches how to build predictive models using real-world datasets, covering classification, regression, and model evaluation techniques.

Learn how businesses can use AI responsibly and effectively, exploring strategy and Microsoft tools like Azure OpenAI Service.

Teaches how to identify natural groupings within data using methods like K-Means and hierarchical clustering.