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Supervised Machine Learning From First Principles
Discussing the principles behind the most used Machine Learning algorithms
This Course Includes
udemy
3.8 (23 reviews )
20 total hours 
english
Online - Self Paced
course
Udemy
About Supervised Machine Learning From First Principles
Machine Learning Principles: Unlocking the Power of Algorithms and Concepts
Are you ready to take your Machine Learning skills to the next level? This course is designed to introduce you to the fundamental principles behind Machine Learning algorithms and concepts, empowering you to become a more effective and insightful practitioner in this rapidly evolving field.
Why This Course?
Machine Learning is more than just a tool – it's a powerful approach to problem-solving that requires a deep understanding of its underlying principles. Without this foundation, you may find yourself:
Struggling to interpret model results effectively
Unsure why one model outperforms another
Unable to choose the most appropriate metrics for your specific problems
Limited in your ability to innovate and create custom solutions
This course aims to bridge the gap between simply using Machine Learning tools and truly mastering the science behind them.
What You'll Learn
Throughout this course, you'll gain invaluable insights into:
The core mathematical and statistical concepts driving Machine Learning algorithms
How to interpret common evaluation metrics (e.g., MSE, accuracy, precision, recall) and understand their real-world implications
The strengths and weaknesses of various Machine Learning models and when to apply them
Techniques for feature selection, preprocessing, and model optimization
The ethical considerations and potential biases in Machine Learning applications
Course Structure
We'll cover a range of topics, including but not limited to:
Regression
Classification
Resampling Methods
Bootstrap
Ensembles
SVMs
Each section includes Python code discussions with suggested homework to reinforce your learning and help you apply these principles to actual problems.
Who Should Take This Course?
This course is ideal for:
Data scientists looking to deepen their theoretical knowledge
Software engineers transitioning into Machine Learning roles
Students pursuing careers in AI and data analysis
Professionals seeking to leverage Machine Learning in their industry
Whether you're just starting your journey in Machine Learning or looking to solidify your understanding, this course will provide you with the insights and skills needed to excel in this exciting field.
What You Will Learn?
- Machine Learning Principles.
- The principles behind Machine Learning algorithms (not just the codes!).
- Regression (Linear Regression, Multiple Linear Regression, Polynomial Regression, and Support Vector Regression).
- Classification (Logistic Regression, k-Nearest Neighbours, Trees, and Support Vector Machines).
- Other principles such as Cross Validation, AIC, BIC, and choosing the right metrics for your algorithm.