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