Foundations of Machine Learning: Concepts, Models & Applications

Foundations of Machine Learning: Concepts, Models & Applications

Artificial intelligence is one of the most important disciplines in the technology space right now, driven by the emergence of generative AI which introduce the field to the masses. A crucial branch of AI is machine learning. If you want to enter the AI sector, mastering machine learning is a major step, as it provides core concepts that apply across various AI fields.

Whether you want to automate repetitive tasks, predict outcomes, or optimize operations, understanding the mechanics of ML is the first step toward true innovation. This course covers all the core concepts of machine learning across 22 lessons divided into six weeks. However, you can study at your own pace, ensuring you grasp each concept fully before moving to the next module.

Why Take This Course?

You might be thinking, “I’m not a mathematician or an engineer, why do I need a machine learning course?” The answer: You don’t necessarily need to be a mathematician or engineer before you can take this course. Here are some reasons this is valuable for a wide range of professional, including those that are not primarily in tech: 

• Learn how to solve problems with data: This course teaches you how to move beyond intuition. You will learn how to train models that find patterns in data that are impossible for humans to spot. This is crucial for more accurate business forecasts.
• Master the Most In-Demand Tech Stack: Python, Jupyter, and Scikit-learn are the gold standards of the industry. You will gain hands-on experience with the exact tools used by top-tier tech companies.
• Bridge the Gap Between Data and Strategy: Learn how to interpret models. You will be able to explain why a model made a certain prediction, which is crucial for gaining stakeholder trust and making informed leadership decisions. 
• Work More Efficiently: Learn how to automate the model-building process using pipelines and tuning, allowing you to spend less time on manual analysis and more time focusing on making your results clear and impactful.
• Build a Complete Portfolio: This isn’t just theory. You will work on hands-on projects, including predicting survival outcomes and building end-to-end pipelines, giving you tangible proof of your skills.

Modules Included

This course is structured to guide you from the foundational definitions of AI to the deployment of advanced predictive pipelines. It includes 22 lessons across 6 weeks, with practical hands-on projects to solidify your learning.

Week 1: Introduction to Machine Learning

This section mainly includes the foundational concepts of machine learning

Module 1: What is Machine Learning?

The module covers the foundational overview of the field, including its history and some of the basic real world use cases. The goal is to ensure everyone, including beginners understand what machine learning is about and what they should expect to do after learning it.

Module 2: Types of Machine Learning

In this module, we will cover all the major types of machine learning, including Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning. These are core model training methods that you will need understand to have a solid comprehension of the field. 

Module 3: ML vs AI vs DL

Understanding the hierarchy and differences between these key terms. Machine learning (ML), Artificial Intelligence (AI), and Deep Learning are three common topics that are often misunderstood. This section will help clarify the differences. 

Module 4: Real-world Applications

How ML is being used today across various industries. This module includes a deep dive of the most practical applications of machine learning at different scales and in different industries. 

Module 5: Tools of the Trade

This is the last module of the introductory section, and it will mainly cover the tools that you will need when learning and implementing machine learning concepts. You will learn how to set up and uses tools like Python, Jupyter, Scikit-learn, and more. 

Week 2: Data & Preprocessing

If you are taking this course, you have probably heard of the saying “a model is as good as the data its trained with.” In this week, we will focus on the heavy lifting of preparing your data for success.

Module 6: Understanding Features and Labels

This module will cover labeling data, which is a foundational step for preparing data. You will also learn how to distinguish between structured and unstructured data, and more. 

Module 7: Feature Engineering (Part 1)

In this module, you will learn about the art of encoding categorical data for your models. It includes techniques like One-Hot Encoding for nominal data and Label Encoding for ordinal data. 

Module 8: Feature Engineering (Part 2)

Module 8 will focus on more advanced techniques for data preparation such as handling missing values, identifying and removing outliers, and creating new derived features to improve the predictive power of your machine learning models.

Module 9: Feature Scaling

In this module, you will master Normalization and Standardization to improve model performance by ensuring all features are on a consistent scale to prevent certain variables from dominating the learning process and helps algorithms converge faster.

Module 10: Validation Strategies

This is the last module of week. It will focus on Train-Test Split and Cross-Validation to ensure reliability.

Week 3: Supervised Learning

Supervised learning is one of the common techniques used to train machine learning models. The modules in this section will focus on this training technique.

Module 11: Linear Regression

By the end of this module, you will have mastered concepts like predicting continuous numerical values (includes code) and how to apply them in real world model training. 

Module 12: Logistic Regression

This module will train you on mastering binary classification for “yes/no” outcomes. You will learn how to use the sigmoid function to map data into probabilities. You will also learn how to set decision thresholds and apply this algorithm to real-world scenarios like spam detection. 

Module 13: Hands-on Mini Project

The final module of the week will introduce you to the first hands on project (Predicting titanic survival). This hands-on project allows you to practice the entire workflow, from handling missing data and encoding passenger classes to training a logistic regression model and evaluating its accuracy.

Week 4: Unsupervised Learning

Week 4 has three modules that focus on Unsupervised Learning, another machine learning model training technique. You will learn how to find hidden patterns and structures in data when you don’t have predefined labels.

Module 14: Clustering: K-Means

Grouping similar data points together automatically. Module 14 will teach you how the K-Means algorithm iteratively assigns data points to clusters by calculating centroids. You will also learn how to choose the optimal number of clusters using the Elbow Method. 

Module 15: Dimensionality Reduction: PCA

Here you will learn how to simplify complex data without losing vital information using Principal Component Analysis (PCA). You will also understand how to reduce the number of variables in a dataset while preserving as much variance as possible. 

Module 16: Hands-on Practice

The final module of week 4 introduces you to our second hands on project—Clustering the Iris Dataset to identify natural groupings. By doing this project, you will learn how to use clustering algorithms to group different species based on their physical measurements. 

Week 5: Decision Trees & Ensemble Models

This week has three modules that explore in detail high-performance modeling using logic-based trees and “wisdom of the crowd” techniques.

Module 17: Decision Trees & Pruning

In this module, you learn the basics of decision trees and pruning, including how trees make decisions and how to prevent overfitting. You will also learn how to combine multiple decision trees into a single “forest” to achieve superior accuracy and stability.

Module 18: Gradient Boosting

Here you will learn how Gradient Boosting builds models sequentially, with each new tree correcting the errors of the previous ones. You will also be introduced to XGBoost, a high-performance implementation of this technique. 

Module 19: Feature Importance

In this module, you will learn how to identify which specific variables have the most significant impact on your model’s predictions. The module will also explore techniques for measuring feature importance and model interpretability, allowing you to explain your results to relevant stakeholders. 

Week 6: Model Evaluation & Tuning

This is the final week of our course. It includes only two modules that focus mainly on measurement and optimization.

Module 20: Performance Metrics

The module explores how to go beyond basic accuracy to evaluate how well your model truly performs. You will master the use of the Confusion Matrix to identify false positives and negatives. You will also learn how to interpret ROC curves and AUC scores to measure a model’s ability to distinguish between different classes effectively.

Module 21: Model Tuning

Here you will explore how to fine-tune your machine learning models to achieve peak performance. You will master techniques like Grid Search and Hyperparameter Optimization to systematically test different configurations. 

Module 22: Final Mini Project—Building an End-to-End ML Pipeline from scratch.

This is the last hands-on project. It will help you consolidate everything you have learned in this course by building a complete, production-ready machine learning pipeline. 

You will do the project from the initial data loading and preprocessing stages through to model selection, hyperparameter tuning, and final evaluation. The goal of the project is to teach you a comprehensive workflow that you can apply to any real-world data challenge.

Who Should Take This Course?

Here a few categories of people that should consider this course: 

• Business Leaders: This includes managers and tech Leads who want to understand how ML can be applied to their business goals.
• IT Professionals: Admins and software engineers that are looking to expand into data automation and predictive security.
• Data Aspirants: Anyone looking to transition into a Data Analyst or Junior Data Scientist role.
• Software Developers: Programmers who want to integrate predictive features into their applications.
• Curious Nerds: Even if you don’t intend to incorporate machine learning into your immediate workflow, this course remains valuable. It provides a foundational understanding of data logic and algorithmic thinking that can enhance your approach to problem-solving in any technical field.

Requirements

• Basic Python Knowledge: Familiarity with variables and basic programming logic
• Basic Computer Literacy: Comfort using a desktop computer (any OS).
• Software: A computer with Python, Jupyter, and all the other basic tools installed (refer to week
• Internet Connection: You will need a stable connection for accessing video lessons and resources.
• No Design Background Required: The course focuses on logic and data over aesthetics.