About the course
This introductory course is designed for enthusiasts from diverse non-computing backgrounds who want to harness the power of machine learning. Whether you're in healthcare, marketing, finance, education, or any other field, this course will equip you with the knowledge and skills to apply machine learning for data and predictive analysis effectively. Participants in this course will be familiarized with basic data pre-processing and exploratory analysis techniques followed by a solid understanding of unsupervised and supervised machine learning algorithms, enabling them to implement classification, regression, and clustering models for various applications. They will also gain practical knowledge of advanced machine learning algorithms like artificial neural networks, allowing them to build, train, and optimize neural network models for complex problems. Each module includes a combination of introductory lectures, hands-on exercises, case studies, and projects to reinforce the concepts and skills learned. The micro-credential with conclude with a Capstone project that will enable you to apply the various concepts learned throughout the course.
What you'll learn
You will learn:
- Data exploration and preprocessing
- Scenario-based machine learning model selection
- Machine learning model development
- Communicate results effectively
Requirements
Pre Requisites:
- Computer Literacy: Proficiency in using computers and standard office software.
- Basic Programming Skills: Basic knowledge of programming especially in Python.
- STEM Background: A background in Science, Technology, Engineering, and Mathematics.
Course Requirements:
- Complete all modules
- Participate in discussions
- Take the assessments
Technical Requirements:
- Reliable internet connection
- Computer or mobile device with webcam
- Audio and video capabilities
- Compatible web browser (e.g., Chrome, Firefox)
Who Should Enroll
- Beginners in Data Science
- Students of computer science, statistics or related fields
- Professionals transitioning into data science and machine learning
- Entry-Level Data and AI Professionals
- Tech Enthusiasts
Course content
Welcome Announcement
Welcome (Start Here)
Facilitator's Bios
Syllabus
Netiquette Policy
Course Communication Expectations
Introduce Yourself
What you Need To Begin?
Module Overview
Lesson: Data Analysis - An Introduction
Reading Assignment
Discussion Forum (Optional Activity)
Quiz (Ungraded): Data Introduction
Lesson: Data Collection and Preparation
Discussion Forum (Optional Activity)
Quiz (Graded): Data Collection & Preprocessing
Lesson: Data Visualization
Lesson: Visual Aspects of Data
Quiz (Ungraded): Visualizing Data
Lesson: Interactivity in Visualization
Lesson: Exploratory Data Analysis (EDA)
Assignment (Graded): Hands-on EDA Activity using Tableau
Discussion with Peers & Instructors: Hands-on EDA Activity using Tableau
Lesson: Introduction to Feature Engineering
Lesson: Dimensionality Reduction
Lesson: Fairness in Data
Discussion Forum (Optional Activity)
Assignment (Graded): Data Preparation
Summative Assessment (Graded): Data Preparation
Quiz (Graded): Feature Engineering
Summary
Module Overview
Lesson: What is Machine Learning?
Quiz (Graded): Machine Learning Basics
Lesson: Real World Applications of Machine Learning
Concept Map Assignment (Graded): Real World Applications of Machine Learning
Discussion with Peers: Will Machines Replace Doctors?
Lesson: Machine Learning Model Lifecycle
Lesson: Popular Tools for Machine Learning
Lesson: Unsupervised Machine Learning
Quiz (Graded): Fundamentals of Unsupervised Machine Learning
Lesson: What is Clustering?
Lesson: K-Means Clustering
Lesson: Types of Clustering
Lesson: Measuring the Goodness of a Cluster
Summative Assignment (Graded): Hands-on Practical on K-Means Clustering
Summative Assessment (Graded): How well did you understand the hands-on practical?
Meet with Your Instructor!!!
Summary
Self Reflective 3-2-1 Activity
Module Overview
Lesson: Supervised Machine Learning
Quiz (Ungraded): Supervised Machine Learning Basics
Lesson: What is Regression?
Lesson: Linear Regression
Assignment (Ungraded): Hands-on Practical on Applying Linear Regression
Quiz (Graded): Regression
Virtual Office Hour
Lesson: What is Classification?
Lesson: Logistic Regression
Assignment (Ungraded): Hands-on Practical on Applying Logistic Regression
Lesson: Confusion Matrix and Performance Metrics
Quiz (Graded): Performance Metrics
Lesson: Loss Functions
Quiz (Ungraded): Loss Functions
Lesson: Popular Machine Learning Algorithms
Quiz (Graded): Popular Machine Learning Algorithms
Mini-Project (Graded): Comparative Analysis of Supervised Machine Learning Algorithms
Discussion with Instructor: Mini-Project
Summary
Module Overview
Lesson: The Artificial Intelligence (AI) Landscape
Lesson: Basics of Artificial Neural Networks
Quiz (Ungraded): Basics of Artificial Neural Networks
Lesson: Perceptron
Quiz (Ungraded): Perceptron
Lesson: Multi-Layer Perceptron
Quiz (Graded): Multi-Layer Perceptron
Question & Answer: Multi-Layer Perceptron
Lesson: Activation Functions
Quiz (Ungraded): Activation Functions
Discussion with Instructor: Activation Functions
Lesson: Loss Functions in Neural Networks
Lesson: Gradient Descent
Lesson: Optimizers
Assignment (Ungraded): Optimizers
Discussion with Peers: Optimizers
Lesson: Back Propagation
Quiz (Graded): Back Propagation
Lesson: Overfitting and Underfitting
Quiz (Ungraded): Overfitting and Underfitting
Lesson: Cross Validation
Quiz (Ungraded): Cross Validation
Lesson: Dropout Regularization
Quiz (Ungraded): Dropout Regularization
Lesson: Model vs. Hyper Parameters Tuning
Assignment (Graded): Model vs. Hyper Parameter Tuning
Summative Task (Graded): Model vs. Hyper Parameter Tuning
Description: Case Study
Discussion with Peers: Case Study
Power of Neural Networks
Summary
Description: Capstone Project
Submission: Capstone Project
Course Completion Annoucement
Course Completion Certificate
Instructors
- RI2 courses43 students
Rabia Irfan
- 2 courses43 students
Momina Moetesum
Dr. Momina Moetesum is an Assistant Professor in Faculty of Computing at the School of Electrical Engineering and Computer Sciences (SEECS), National University of Sciences and Technology (NUST), Pakistan. She completed her Ph.D. in Computer Science in 2021 from Bahria University, Islamabad, Pakistan. During her Ph.D. she secured the Hubert Curien PERIDOT Research Grant and worked in LIPADE, Université Paris Cité, France, on her research titled “Deformation Estimation and Classification of Graphomotor Impressions-An Application to Neuropsychological Assessments”. Her research interests include Online and Offline Handwriting Analysis for Brain Disorder Identification, DocumentAI, Computer Vision, and Natural Language Processing. She was the recipient of Best Poster Award at the Doctoral Consortium organized by ICDAR in 2017 in Kyoto, Japan. She co-organized the programming events at 3rd IAPR TC10/TC11 Summer School on Document Analysis (SSDA2019) held in Islamabad, Pakistan in 2019. She has been serving as program committee member in ICDAR since 2017 and was the publication chair in ICFHR 2023. She is also a member of IAPR standing committee on Equality, Diversity, and Inclusion. She has published 30+ articles in reputed conferences and Impact Factor journals. Dr. Momina is an expert in Artificial Intelligence, Machine Learning, Deep Learning, and Computer Vision. She has been associated with academia for the past 10 years. She has conducted several trainings in machine learning and computer vision to help individuals gain the required skills.
- JY1 courses43 students
Junaid Younas
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Enrolment options
This introductory course is designed for enthusiasts from diverse non-computing backgrounds who want to harness the power of machine learning. Whether you're in healthcare, marketing, finance, education, or any other field, this course will equip you with the knowledge and skills to apply machine learning for data and predictive analysis effectively. Participants in this course will be familiarized with basic data pre-processing and exploratory analysis techniques followed by a solid understanding of unsupervised and supervised machine learning algorithms, enabling them to implement classification, regression, and clustering models for various applications. They will also gain practical knowledge of advanced machine learning algorithms like artificial neural networks, allowing them to build, train, and optimize neural network models for complex problems. Each module includes a combination of introductory lectures, hands-on exercises, case studies, and projects to reinforce the concepts and skills learned. The micro-credential with conclude with a Capstone project that will enable you to apply the various concepts learned throughout the course.
- Teacher: Rabia Irfan
- Teacher: Momina Moetesum
- Teacher: Junaid Younas
- Enrolled students: 43
This course includes
Assignments
Custom certificates
Forums
Quizzes
Resources