Module 0: Welcome to Machine Learning
Welcome to the course on Machine Learning! In this module you will learn about what Machine Learning is? Who is this course for? What this course contains and how will you be able to benefit from this course. This introductory module will give you information on the instructor’s profile, course syllabus and objectives, different features of the course, grading policies, expectations around academic honesty, frequently asked questions, and a chance to chat with your peers.
- 1 units
- 1 hour
Hide breakdown
Discussion
Meeting Your Peers
Module 1: Introduction to Machine Learning
This module will uncover the wonderful world of machine learning, demonstrating its ubiquity in our lives and explaining its underlying concepts. Through a mix of theory and examples, this module will give you a comprehensive understanding of machine learning's key concepts, historical background, applications, challenges and how it can be harnessed for social good. The module will also give you an opportunity to learn the basics of python and apply them through a programming assessment.
Hide breakdown
Assessment
Programming Assessment 1
Module 2: Supervised Learning
Supervised learning is one of the fundamental techniques in Machine Learning. This module will equip you with the foundational knowledge and practical skills necessary to apply supervised learning algorithms to real-world problems. Through a combination of theoretical concepts and hands-on exercises, you will gain a solid understanding of the principles, algorithms, and evaluation methods involved in supervised learning.
Hide breakdown
Discussion
Supervised Learning
Module 3: KNN
K-NN is a non-parametric method used for both classification and regression tasks. This module will familiarize you with the underlying principles, implementation, and evaluation of the K-NN algorithm. Through theoretical explanations and practical examples, you will gain proficiency in applying K-NN to real-world problems, selecting an appropriate value for K, handling distance metrics, dealing with imbalanced data, and optimizing model performance.
- 32 units
- 3 hours 30 minutes
Hide breakdown
Assessment
2 Graded Quizzes
Module 4: Evaluation of Classifiers
The module provides a comprehensive understanding of essential evaluation metrics for classification tasks. You will begin with learning about accuracy, build up to precision, recall, and F1-score, which are widely used performance measures that assess the effectiveness of classifiers in predicting class labels. This module will equip you with the knowledge and skills to calculate and interpret these metrics accurately. You will gain a solid understanding of the concepts behind precision (the proportion of correctly predicted positive instances), recall (the proportion of actual positive instances correctly predicted), and F1-score (a harmonic mean of precision and recall). Through practical examples and exercises, you will learn how to apply these metrics to assess classifier performance and make informed decisions based on their results
Hide breakdown
Discussion
Evaluating Classifiers
Assessment
Programming Assessment 2
Module 5: Linear Regression
In this module, you will gain a comprehensive understanding of linear regression, a widely-used technique in predictive modeling. You will learn the fundamental principles and assumptions of linear regression, including linearity and independence. The module will also focus on parameter estimation, coefficient interpretation, and prediction. Additionally, important topics like regularization techniques will be explored. Through hands-on exercises and real-world datasets, you will develop practical skills in building, evaluating, and improving linear regression models, enabling you to analyze data, make accurate predictions, and extract valuable insights.
Hide breakdown
Discussion
Linear Regression
Assessment
Programming Assessment 3
Module 6: Logistic Regression
Logistic regression is a powerful tool used to predict the probability of a binary outcome based on a set of input variables. In this module you will cover the underlying concepts and assumptions of logistic regression, including the logistic function and loss function. You will also explore the process of model fitting, parameter estimation, and interpretation of results. Practical examples and hands-on exercises are included to enhance your understanding and application of logistic regression in real-world scenarios. By the end of the module, you will have a solid foundation in logistic regression and you will be equipped to utilize this technique for predictive modeling and decision-making tasks.
- 23 units
- 5 hours 30 minutes
Hide breakdown
Discussion
Logistic Regression
Assessment
Programming Assessment 4
Module 7: Neural Networks
The module on Neural Networks provides you with an introduction to this powerful machine learning technique that mimics the structure and functioning of the human brain. Neural networks are composed of interconnected nodes, or artificial neurons, organized in layers that process and transform data. Here you will cover the fundamental concepts and components of neural networks, including activation functions, weight initialization, forward and backward propagation, and gradient descent optimization.
Hide breakdown
Discussion
Neural Networks
Assessment
Programming Assessment 5
Module 8: Support Vector Machines
The module on Support Vector Machines (SVM) offers an introduction to this powerful supervised learning algorithm used for classification and regression tasks. SVMs aim to find the optimal hyperplane that separates data points of different classes with the largest margin. In this module you will learn about the underlying principles of SVM, including the concept of support vectors, kernel functions, and the margin optimization objective. You will explore both linear and nonlinear SVMs, highlighting their strengths and limitations.
Hide breakdown
Discussion
Support Vector Machines
Module 9: Bayes Theorem
This module provides you with an introduction to Bayes Theorem, a fundamental concept in probability theory and statistics. Bayes Theorem allows us to update our beliefs about the probability of an event based on new evidence or information. The content sheds light on the core components of Bayes Theorem, including prior probabilities, likelihoods, and posterior probabilities. It also explores how Bayes Theorem can be applied to various scenarios, such as medical diagnostics and spam filtering.
- 18 units
- 1 hour 30 minutes
Module 10: Naive Bayes Classifier
This module introduces the Naive Bayes classifier, a simple yet effective probabilistic algorithm used for classification tasks. The Naive Bayes classifier is based on Bayes' theorem and makes the assumption of independence among features. Here, you will cover the key concepts and workings of the Naive Bayes classifier, including the calculation of prior probabilities, likelihoods, and posterior probabilities.
Hide breakdown
Discussion
Naive Bayes Classifier
Assessment
Graded Quiz 10
Assessment
Programming Assessment 6
Module 11: Responsible AI and Machine Learning for Development
This module aims to unveil the 'black box' nature of artificial intelligence and machine learning models, enabling deeper understanding of their inner workings and addressing the multifaceted issues related to AI ethics, fairness and explainability. It covers fairness in AI, interpretability of ML models, sources of bias and techniques to mitigate bias. The module also touches upon ethics in AI to understand the moral principles guiding AI development and its use. Lastly, the content covers machine learning for development, explaining how ML techniques can be used to address social and economic challenges in developing countries.
- 24 units
- 2 hours 30 minutes
Hide breakdown
Discussion
Responsible AI and Machine Learning for Development
Assessment
Graded Quiz 11