Unit I

Unit I – Basics of Data Analytics through through Programming Languages
Introduction to programming languages: Covering the core concepts and syntax of a programming language (e.g., Python) for beginners.
Data Handling with a programming language (e.g., Python): Utilizing the programming language’s (e.g., Python’s) essential tools and techniques, including the Pandas and Matplotlib libraries, for data discovery, visualization, manipulation, and cleaning.
Machine Learning Landscape: A comprehensive overview of the field of machine learning, outlining key concepts, techniques, and applications.
Data Challenges in ML: Addressing common issues encountered in machine learning datasets, such as missing values, noise, and imbalanced data.

Unit II

Unit II – Introduction to ML through Programming Languages
Overview of Learning Paradigms: Exploring the distinctions between supervised, unsupervised, and reinforcement learning methodologies.
Understanding the Bias-Variance Tradeoff: An introductory guide to balancing bias and variance to prevent overfitting and underfitting in machine learning models.
Exploring Key Machine Learning Algorithms: A primer on various machine learning algorithms including linear regression, decision trees, random forests, and ensemble learning approaches.
Examples of Classification and Regression: Demonstrating practical applications of machine learning through classification and regression scenarios.

Unit III

Unit III – Training and Testing of Data Using ML Algorithms
Building Fundamental ML models with a programming language (e.g., Python): Covers the creation of simple machine learning models, including the application of feature scaling and the development of training and testing data splits.

Unit IV

Unit IV – Evaluating the Performance of the Algorithm
Assessment of ML Model Effectiveness: Utilizing various performance indicators to measure the success of machine learning models.
Practical Application of Learned Concepts: Engaging in hands-on exercises to apply and reinforce the knowledge gained.

Prerequisite: A foundational understanding of educational psychology, computer science principles, and basic research methodologies.

Course Objectives:

• Master Data Preprocessing Techniques: Equip students with the skills to clean, manipulate, and prepare for using real-world data for analysis, particularly emphasizing educational datasets
• Explore Machine Learning Techniques: Introduce supervised, unsupervised, and reinforcement learning, and discuss their applicability and implementation in Python for educational data analysis.
• Implement Machine Learning Algorithms: Develop hands-on proficiency in applying machine learning algorithms to datasets, emphasizing the process from data preprocessing to algorithm selection and implementation.
• Evaluate Model Performance: Teach students to assess the performance of machine learning models using various metrics, enabling them to understand model effectiveness and areas for improvement in the context of educational challenges

Course Outcomes:

CO1: Students will be able to preprocess data, handling issues like missing values, noise, and sampling biases, to make it suitable for machine learning analysis.
CO2:Students will learn to select and apply appropriate machine learning algorithms for various educational problems, using Python as the primary tool.
CO3: Students will be able to critically analyze the outputs of machine learning models and use this analysis to propose improvements or interventions in educational settings.

Skills:
Critical Analysis: Skill in analyzing and evaluating ed-tech tools and methodologies, including machine learning model outcomes.
Research and Design: Proficiency in executing Learning Engineering research, covering machine learning application, data handling, and analysis.
Evidence-Based Decision Making: Ability to use evidence-based strategies for tackling educational challenges with machine learning tools.
Interdisciplinary Synthesis: Competence in merging knowledge from educational science, psychology, computer science, and machine learning for Learning Engineering.
Innovative Solutions Development: Skill in creating and proposing unique Learning Engineering solutions, utilizing machine learning for specific educational needs.

Course Outcomes (CO) – Program Outcomes (PO) Mappings

Evaluation Pattern:

*CA – Can be Quizzes, Assignment, Projects, and Reports, and Seminar

1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition Aurelien Geron O’Reilly
2. Russell and Norvig, “Artificial Intelligence: A Modern Approach”, Prentice Hall.
3. http://www.stanford.edu/class/cs229/materials.html
4. http://www.stanford.edu/class/cs221/handouts.html
5. Christopher Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006. [6.] Richard Sutton and Andrew Barto, “Reinforcement Learning: An Introduction”, MIT
   Press, 1998.