Foundations of Artificial Intelligence Certification Course

Duration: 6-Week 

Level: Beginner

Week 1 Lecture: Introduction to Artificial Intelligence

Definitions, History, and Evolution of AI

Week 1: Introduction to Artificial Intelligence

Types of AI: Narrow AI, General AI, and Superintelligence

 

Week 1 Lecture: Key Milestones and Real-World Applications of Artificial Intelligence

Today, we’re going to explore a question you’ve probably heard a lot: What is AI? 

Capstone Project: Weekly Milestones Guide

Throughout this course, you will work on an end-to-end AI project. You are encouraged to make weekly progress on the following milestones. You will submit your completed project—including all code, documentation, and your SRS—at the end of Week 6.

 

Test your knowledge of the basics of Artificial Intelligence with this 40-question multiple choice quiz. This quiz covers key concepts from Week 1, including AI definitions, history, types of AI, important milestones, and real-world applications.

Python syntax, data types, and control structures

Python is a high-level, general-purpose programming language designed for readability, simplicity, and flexibility. It uses clear, English-like syntax and significant indentation, making it easy to write and understand code-even for beginners. Python supports multiple programming paradigms, including object-oriented, procedural, and functional programming, and comes with a comprehensive standard library and access to hundreds of thousands of third-party packages.

Week 2: Python Programming Basics – Functions & Modular Code

What Are Functions and Why Do We Use Them?

Imagine you have to bake cookies for your friends every week. If you had to write out the entire recipe from scratch each time, it would take a lot of effort and you might make mistakes. Instead, you keep a recipe card that you can use over and over. In programming, a function is like that recipe card. It’s a set of instructions that you write once and can use as many times as you want. This helps keep your code neat, organized, and easy to read.

Week 2: Python Programming Basics: File I/O & Data Handling

What Is File I/O and Why Does It Matter?

File I/O (Input/Output) is how programs read data from files (like text documents or spreadsheets) and save results back to them. Imagine your program is like a chef-file I/O lets the chef read recipes (input) and write down the final dish (output). For AI, this is critical because AI models need data to learn, and that data often comes from files.

Python: The Language of AI (Audio Lesson)

A beginner QuickStart guide to AI development with Python.

Week 3: Core Machine Learning Concepts

What is Machine Learning?

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and improve their performance on tasks without being explicitly programmed. 

Week 3: Core Machine Learning Concepts

ML Features, Labels, and Datasets

Machine learning transforms raw data into actionable insights, much like a chef turns ingredients into a meal. At the heart of this transformation lie three fundamental concepts: features, labels, and datasets. These elements form the building blocks that enable machines to learn patterns, make predictions, and solve complex problems.

Week 3: Core Machine Learning Concepts
Key Algorithms - Regression (Linear Regression Basics)

Linear regression is one of the most fundamental and widely used algorithms in machine learning and statistics. It serves as the cornerstone for understanding relationships between variables and making predictions based on observed data. At its core, linear regression models the relationship between a dependent variable (the outcome we want to predict) and one or more independent variables (the inputs used for prediction).

This video is the introduction to Jon Krohn’s academic Machine Learning Foundations series. Dr. Krohn is a chief data scientist, bestselling author, and university lecturer.

Week 3: Machine Learning Foundations
Machine Learning Workflow

In the world of machine learning, the phrase "garbage in, garbage out" is more than just a saying, it is a fundamental truth. The quality of data that goes into a machine learning model directly determines the quality of its predictions and insights. Data preparation and cleaning are the critical first steps in the machine learning workflow, ensuring that the information fed to algorithms is accurate, consistent, and relevant. Without proper preparation, even the most advanced models will struggle to make sense of the data, leading to unreliable or even misleading results.

This lecture explains the importance of evaluation metrics in machine learning, including accuracy, precision, recall, F1-score, and regression metrics.

Week 3: Machine Learning Foundations
Machine Learning Workflow: Introduction to Scikit-learn

Scikit-learn, often abbreviated as sklearn, is one of the most widely used open-source machine learning libraries in Python. It provides simple and efficient tools for data analysis, preprocessing, modeling, and evaluation, making it accessible to both beginners and experts. Built on top of foundational Python libraries like NumPy, SciPy, and Matplotlib, Scikit-learn integrates seamlessly with the Python data science ecosystem. 

Week 3 Quiz: Machine Learning Foundations This assessment evaluates your understanding of the core principles and practical skills covered in Week 3 of the Foundations of Artificial Intelligence Certification Program. The test focuses on essential machine learning concepts, including supervised and unsupervised learning, features and labels, model evaluation metrics, key algorithms (regression, classification, clustering), and the end-to-end workflow of building, training, and testing machine learning models. You will also demonstrate familiarity with Python-based tools such as Scikit-learn and best practices for preparing, splitting, and validating datasets. Successfully completing this test confirms your readiness to apply foundational machine learning techniques in real-world AI projects.

This concise IBM video clearly explains the structure and function of artificial neural networks, including input, hidden, and output layers, and the analogy to the human brain.

What is a Neural Network?

A neural network is a computational model inspired by the structure and function of the human brain, designed to recognize patterns, make decisions, and solve complex problems. At its core, a neural network consists of interconnected processing units called artificial neurons (or nodes), organized into layers that collaboratively transform input data into meaningful outputs.

Biological Inspiration and Artificial Neurons

The development of artificial neural networks (ANNs) is deeply rooted in our understanding of biological neural systems.

Perceptrons: The Building Blocks of Neural Networks

A perceptron, introduced by Frank Rosenblatt in 1957, is the simplest form of an artificial neural network and a foundational concept in machine learning.

Introduction to Activation Functions

Activation functions are the cornerstone of neural networks, transforming linear computations into non-linear decision-making tools. Without activation functions, neural networks would collapse into linear regression models, incapable of capturing complex patterns in data like image textures, speech intonations, or financial trends.

• This video succinctly explains the architecture and flow of data in a feedforward neural network, including input, hidden, and output layers.

• This video demonstrates why hidden layers are needed, how data moves forward through the network, and the role of activation functions and weights.

Introduction to Backpropagation and Gradient Descent

Backpropagation and gradient descent form the backbone of neural network training, enabling machines to learn from data by minimizing prediction errors. Gradient descent is an optimization algorithm that iteratively adjusts model parameters (weights and biases) to find the minimum of a cost function, which quantifies the difference between predicted and actual outputs.

Introduction to Overfitting and Regularization

Overfitting is a fundamental challenge in machine learning where a model performs exceptionally well on training data but fails to generalize to unseen data. This occurs when the model learns noise, outliers, or irrelevant patterns specific to the training set, effectively "memorizing" the data rather than understanding underlying trends.

Introduction to Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a specialized class of deep learning models designed to process grid-structured data, such as images, videos, and audio spectrograms.

Introduction to Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed to process sequential data by maintaining an internal "memory" of previous inputs.

Introduction: The Importance of Architecture Selection

Choosing the right neural network architecture is critical to solving real-world problems efficiently and effectively.

 

• This video provides practical, easy-to-understand guidance on selecting the right neural network architecture for a given problem.

• This video discusses how to choose between CNNs, RNNs, and other architectures based on data type (images, text, sequences), task complexity, and other factors.

The Critical Role of Model Deployment

Model deployment marks the transition from theoretical machine learning to practical, real-world impact. It involves integrating a trained model into a production environment where it can process inputs and deliver actionable outputs, such as predictions, classifications, or recommendations.

Deep Learning in Healthcare: Transforming Patient Care

Deep learning has revolutionized healthcare by enhancing diagnostic accuracy, streamlining workflows, and enabling personalized treatments. 

Model Deployment & Real-World Applications – Challenges in Deploying AI Systems (Scalability, Latency, Interpretability)

Deploying AI systems in real-world scenarios introduces complex technical and operational challenges that extend far beyond model training. Three critical hurdles—scalability, latency, and interpretability—often determine whether AI solutions succeed or fail in production environments. Understanding these challenges is essential for bridging the gap between theoretical models and practical implementations.

 

AI Model Fairness, Explainability, and Bias Mitigation

Understanding AI bias and fairness

Understanding AI bias and fairness is critical for developing ethical and effective machine learning systems. Bias in AI refers to systematic errors or prejudices that lead to unfair outcomes for specific individuals or groups, often perpetuating historical inequalities or introducing new forms of discrimination.

This video provides a succinct, practical explanation of how LIME works to make complex models interpretable. It covers the algorithm, its strengths, and its limitations.

AI Model Fairness, Explainability, and Bias Mitigation

Techniques for Model Explainability (SHAP, LIME)

Explainability techniques like SHAP and LIME are essential for demystifying AI decision-making processes, particularly in high-stakes domains where transparency impacts trust and regulatory compliance.

AI Model Fairness, Explainability, and Bias Mitigation

Strategies for Mitigating Bias in Datasets and Models

Mitigating bias in AI systems requires a multi-stage approach spanning data collection, model development, and post-deployment monitoring.

Test your understanding of week 4's learning objectives with this matching quiz.

What Are Kernel Methods?

Kernel methods are a class of machine learning algorithms designed to solve non-linear problems by implicitly mapping data into high-dimensional feature spaces.

Support Vector Machines (SVMs)

Concept of the Margin and Support Vectors

Support Vector Machines (SVMs) are supervised learning models that maximize the margin—the distance between the decision boundary (hyperplane) and the nearest data points from each class.

Comparing SVMs and Neural Networks

When to Use SVMs vs. Neural Networks

Choosing between Support Vector Machines (SVMs) and Neural Networks (NNs) requires understanding their inherent strengths relative to problem constraints.

 

Model Evaluation and Selection

Cross-Validation (k-Fold, Stratified)

Cross-validation is a cornerstone technique for assessing model generalizability and mitigating the pitfalls of overfitting.

Hyperparameter Tuning

Grid Search and Random Search

Hyperparameter tuning optimizes model performance by systematically exploring configurations that balance bias and variance. 

Ethical Considerations in Model Selection

Fairness and Transparency in Advanced Models

Ethical model selection requires prioritizing fairness and transparency to ensure AI systems do not perpetuate harm or inequity.

• This video is current (2025) and directly addresses fairness, transparency, and bias mitigation in AI systems.

• This video discusses how data selection, validation, and continuous evaluation are essential for building fair and transparent AI, including applications in healthcare, finance, and facial recognition.

• This video highlights the importance of responsible AI deployment and the practical steps organizations must take to ensure ethical outcomes.

Test your understanding of week 5's learning objectives with this matching quiz.

This comprehensive multiple choice quiz assesses your understanding of the entire Foundations of Artificial Intelligence Certification course. The quiz covers AI history, Python programming, machine learning and deep learning concepts, model evaluation, ethical and responsible AI, and practical coding knowledge.