Fundamentals of Machine Learning
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Fundamentals of Machine Learning
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What you'll learn
Understand core machine learning techniques such as regression, classification, and decision trees
Gain practical experience in model evaluation through techniques like cross-validation and bootstrap
Explore advanced methods in deep learning and neural networks for solving complex tasks
Apply machine learning models to real-world datasets and interpret their performance
Details to know
February 2026
4 assignments
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There are 3 modules in this course
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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course offers a comprehensive foundation in machine learning, taking you through both the theoretical and practical aspects of this powerful field. By learning the fundamentals of algorithms, models, and techniques, you will gain the skills to design, implement, and assess machine learning systems effectively. Throughout the course, you'll dive deep into various methods, including regression, classification, decision trees, SVM, deep learning, and more. The course is structured into lectures, hands-on labs, and deep learning-focused modules. It starts with foundational concepts such as statistical learning and progresses to complex models like neural networks and support vector machines. You'll also explore practical tools like Principal Component Analysis (PCA), random forests, and classification metrics, helping you build confidence in both theory and application. Ideal for those new to the field of machine learning, the course assumes no prior experience in programming or data science. However, a basic understanding of algebra and statistics will be beneficial. It's designed for learners at all levels, providing an accessible entry point into machine learning while offering deep technical insights for more experienced students. By the end of the course, you will be able to implement machine learning models, use deep learning techniques, assess model performance, and apply machine learning methods to real-world datasets.
In this module, we will explore the foundational principles of machine learning, from the basics of statistical learning to advanced techniques like decision trees and deep learning. You will learn essential concepts such as linear regression, classification, and the importance of model selection to prevent overfitting. By the end, you will gain a comprehensive understanding of how machine learning works and the tools used to build robust models.
What's included
14 videos1 reading
14 videos•Total 403 minutes
- Welcome•2 minutes
- Introduction•9 minutes
- Basics in Statistical Learning•43 minutes
- Linear Regression•39 minutes
- Classification•23 minutes
- Sampling and Bootstrap•15 minutes
- Model Selection•35 minutes
- Going Beyond Linearity•9 minutes
- Tree-Based Methods – Part 1•37 minutes
- Tree-Based Methods – Part 2•38 minutes
- Support Vector Machine (SVM)•22 minutes
- Deep Learning•58 minutes
- Unsupervised Learning•52 minutes
- Classification Metrics•23 minutes
1 reading•Total 10 minutes
- Full Course Resources•10 minutes
In this module, we will dive into hands-on labs where you will apply theoretical knowledge to solve real-world machine learning problems. You will work with popular algorithms such as linear regression, SVM, and decision trees, experimenting with techniques like PCA for data reduction and building deep learning models like CNNs. By the end, you will be able to build and fine-tune machine learning models to handle diverse datasets.
What's included
10 videos1 assignment
10 videos•Total 119 minutes
- Linear Regression•21 minutes
- Logistic Regression•15 minutes
- Ridge•10 minutes
- Decision Tree•8 minutes
- Random Forests•9 minutes
- Support Vector Machine (SVM)•10 minutes
- Multilayer Perceptron (MLP)•21 minutes
- CNN•10 minutes
- PCA•6 minutes
- ROC-AUC•9 minutes
1 assignment•Total 15 minutes
- Labs - Assessment•15 minutes
In this module, we will focus on deep learning, specifically Large Language Models (LLMs), and their applications. You will gain practical experience with powerful SDKs like OpenAI and LangChain, learning to build and optimize LLM agents for real-world scenarios. By the end of the module, you will have a deeper understanding of LLMs and be equipped to deploy them effectively using advanced tools and techniques.
What's included
8 videos3 assignments
8 videos•Total 110 minutes
- Deep Learning – Part 1 – LLM Basics•16 minutes
- Deep Learning – Part 2 – LLM Intermediate•20 minutes
- Deep Learning – Part 3 – LLM Agent – OpenAI SDK – Session 1•15 minutes
- Deep Learning – Part 3 – LLM Agent – OpenAI SDK – Session 2•11 minutes
- Deep Learning – Part 3 – LLM Agent – OpenAI SDK – Session 3•10 minutes
- Deep Learning – Part 4 – LLM Agent – LangChain SDK – Session 1•14 minutes
- Deep Learning – Part 4 – LLM Agent – LangChain SDK – Session 2•10 minutes
- Deep Learning – Part 4 – LLM Agent – LangChain SDK – Session 3•13 minutes
3 assignments•Total 80 minutes
- Deep Learning - Assessment•15 minutes
- Full Course Assessment•50 minutes
- Full Course Practice Assessment•15 minutes
Instructor
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Frequently asked questions
Machine learning is a branch of artificial intelligence that focuses on developing algorithms that allow computers to learn from and make decisions based on data. It’s relevant because it powers a wide range of technologies, from self-driving cars to recommendation systems, and is crucial in fields like healthcare, finance, and technology.
This specialization covers the fundamentals of machine learning, starting from basic concepts like linear regression and classification to advanced topics such as deep learning and large language models (LLMs). It includes both theoretical lessons and hands-on labs to help you build practical skills in applying machine learning algorithms to real-world problems.
After completing this specialization, you'll have a strong understanding of machine learning concepts, including statistical learning, model selection, and deep learning techniques. You'll be able to implement various machine learning models such as linear regression, decision trees, and support vector machines, as well as apply advanced techniques like deep learning and LLMs to solve complex problems.
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