What will you in the Machine Learning Foundations: A Case Study Approach Course
Understand real-world applications of machine learning
Distinguish between regression, classification, clustering, and recommendation systems
Apply machine learning techniques using Python and Turi Create
Evaluate model performance using appropriate metrics
Build end-to-end ML applications from data preprocessing to deployment
Program Overview
1. Welcome
Duration: 3 hours
Introduction to machine learning and its business impact
Overview of tools like Python, Jupyter Notebook, and Turi Create
Preview of case study-driven learning structure
2. Regression: Predicting House Prices
Duration: 3 hours
Introduction to regression and its use in predicting house prices
Feature selection, model training, and evaluation
Implementation using real datasets
3. Classification: Analyzing Sentiment
Duration: 3 hours
Basics of classification with a focus on sentiment analysis
Text feature extraction and Naive Bayes classification
Evaluation of prediction accuracy
4. Retrieval: Finding Similar Documents
Duration: 3 hours
Introduction to similarity-based search
Document representation and nearest neighbor methods
Use cases in recommendation and content discovery
5. Recommender Systems: Recommending Products
Duration: 3 hours
Collaborative filtering and matrix factorization
Building recommendation models
Evaluation metrics for recommender systems
6. Deep Learning: Searching for Images
Duration: 3 hours
Intro to deep learning and neural networks
Image classification and feature extraction
Image similarity and search systems
7. Summary and Review
Duration: 2 hours
Recap of key concepts and models
Guidance on advancing further in ML
Final quiz and peer review
Get certificate
Job Outlook
Aspiring Data Scientists: Gain a foundational understanding of ML techniques
Software Developers: Learn to integrate ML features into applications
Business Analysts: Use ML for smarter decision-making
Researchers: Apply ML methods to large data problems
Students: Build a base for AI and data science career paths
Explore More Learning Paths
Expand your machine learning expertise with these carefully curated courses designed to help you build practical skills and apply algorithms to real-world problems.
Related Courses
Machine Learning with Python Course – Learn to implement machine learning algorithms using Python and apply them to diverse datasets.
Machine Learning for All Course – Gain a broad understanding of machine learning concepts and applications, regardless of technical background.
Applied Machine Learning in Python Course – Develop hands-on skills in building and deploying machine learning models with Python.
Related Reading
What Is Data Management? – Explore data management strategies that support accurate analysis and machine learning workflows.
Specification: Machine Learning Foundations: A Case Study Approach Course
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