Supervised Machine Learning From First Principles

Supervised Machine Learning From First Principles, available at $19.99, has an average rating of 3.85, with 79 lectures, based on 23 reviews, and has 8994 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginners who are curious to start their understanding of Machine Learning without jumping head-first into the codes It is particularly useful for Beginners who are curious to start their understanding of Machine Learning without jumping head-first into the codes.

Enroll now: Supervised Machine Learning From First Principles

Summary

Title: Supervised Machine Learning From First Principles

Price: $19.99

Average Rating: 3.85

Number of Lectures: 79

Number of Published Lectures: 79

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: R319.99

Quality Status: approved

Status: Live

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

Who Should Attend

Beginners who are curious to start their understanding of Machine Learning without jumping head-first into the codes

Target Audiences

Beginners who are curious to start their understanding of Machine Learning without jumping head-first into the codes

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.

Course Curriculum

Chapter 1: Introduction to Machine Learning

Lecture 1: Introduction

Chapter 2: Introduction to Statistical Learning

Lecture 1: Modelling for Prediction versus Modelling for Inference

Lecture 2: Parametric versus Non-Parametric Methods

Lecture 3: Trade-off Between Model Accuracy and Model Interpretability

Lecture 4: Supervised versus Unsupervised Learning

Lecture 5: Regression versus Classification

Lecture 6: Assessing Model Accuracy – Measure of Fit

Lecture 7: Bias-Variance Trade-Off

Lecture 8: Assessing Model Fit – Classification Setting

Lecture 9: Classification Example – K-Nearest Neighbours (kNN)

Lecture 10: Confidence Intervals for Coefficient Estimates for Simple Linear Regression Mode

Lecture 11: Hypothesis Test of Coefficient Estimates for Simple Linear Regression

Lecture 12: Accuracy of Coefficient Estimates for Simple Linear Regression

Lecture 13: Estimating Simple Linear Regression's Model Coefficients

Chapter 3: Linear Regression

Lecture 1: Introduction to Linear Regression

Lecture 2: Accessing Simple Linear Regression Model Accuracy

Lecture 3: Residual Standard Error (RSE)

Lecture 4: R-Squared Statistic

Lecture 5: Multiple Linear Regression

Lecture 6: Estimating Multiple Linear Regression Coefficients

Lecture 7: Question 1 – Is There a Relationship Between Response and Predictors

Lecture 8: Question 2 – Variable Selection

Lecture 9: Question 3 – Model Accuracy

Lecture 10: Dealing with Qualitative Variables

Lecture 11: Including Interaction Terms in the Model (Non-Additive Models)

Lecture 12: Including Non-linear Terms in the Model

Lecture 13: Problem #1 – Non-linearity of the data

Lecture 14: Problem #2 – Correlation of Error Terms

Lecture 15: Problem #3 – Non-constant variance of error terms

Lecture 16: Problem #4 – Outliers

Lecture 17: Problem #5 – High leverage points

Lecture 18: Problem #6 – Collinearity

Lecture 19: Python Code Discussion – Regression

Chapter 4: Classification

Lecture 1: Introduction to Classification

Lecture 2: Why Linear Regression Will Not Work

Lecture 3: Introduction to Logistic Regression

Lecture 4: The Logistic Model

Lecture 5: Estimating Logistic Regression Coefficients – Maximum Likelihood Method

Lecture 6: Making Predictions With Logistic Regression

Lecture 7: Multiple Logistic Regression

Lecture 8: Introduction to Linear Discriminant Analysis (LDA)

Lecture 9: Bayes' Theorem of Classification

Lecture 10: Linear Discriminant Analysis One Predictor

Lecture 11: Linear Discriminant Analysis with More Predictors

Lecture 12: The Confusion Matrix, Sensitivity and Specificity

Lecture 13: The ROC Curve

Lecture 14: Quadratic Discriminant Analysis (QDA)

Lecture 15: Python Code Discussion – Classification

Chapter 5: Validation and The Bootstrap Methods

Lecture 1: Introduction to Resampling Methods

Lecture 2: Method 1 – Validation Set Approach

Lecture 3: Method 2 – Leave One Out Cross Validation (LOOCV)

Lecture 4: Method 3 – k-Fold Cross Validation

Lecture 5: Cross Validation for Classification Settings

Lecture 6: The Bootstrap Method

Chapter 6: Linear Model Selection and Regularization

Lecture 1: Introduction to Model Selection and Regularization

Lecture 2: Method 1: Best Subset Method

Lecture 3: Method 2a: Forward Stepwise Selection Method

Lecture 4: Method 2b: Backward Stepwise Selection Method

Lecture 5: Model Selection – Choosing the Optimal Model

Lecture 6: Method 1: Cp Estimate for Test Error

Lecture 7: Method 2: Akaike Information Criterion (AIC) Estimate For Test Error

Lecture 8: Method 3: Bayesian Information Criterion (BIC) Estimate For Test Error

Lecture 9: Method 4: Adjusted R-Squared For Test Error

Lecture 10: Method 5: Validation Set and Cross Validation Estimates For Test Error

Lecture 11: Introduction to Shrinkage Methods

Lecture 12: Ridge Regression

Lecture 13: Ridge Regression Example

Lecture 14: The Lasso

Lecture 15: The Lasso Example

Lecture 16: Mathematical Optimisation of Ridge Regression and the Lasso

Lecture 17: Introduction to Dimension Reduction Methods

Lecture 18: Principal Component Analysis (PCA)

Lecture 19: Principal Component Analysis (PCA) Example

Lecture 20: How PCA Actually Reduces Dimensions (Scree Plots)

Lecture 21: Principal Components Regression

Chapter 7: Tree Based Methods

Lecture 1: Introduction to Tree Based Methods

Lecture 2: Regression Decision Trees

Lecture 3: Predicting Using a Regression Decision Tree

Lecture 4: Pruning Decision Trees

Instructors

Houston Muzamhindo
Data Scientist

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2 stars: 1 votes
3 stars: 5 votes
4 stars: 10 votes
5 stars: 7 votes

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