Machine Learning Foundations

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What Will You Learn?

In this course, you will get hands-on experience with machine learning from a series of practical case-studies.  At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.  Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.

About This Course

Provider: Coursera
Format: Online
Duration: 18 hours to complete [Approx]
Target Audience: Beginners
Learning Objectives: Able to build an end-to-end application that uses machine learning after completing this free course
Course Prerequisites: NA
Assessment and Certification: NA
Instructor: University of Washington
Key Topics: Python Programming, Machine Learning Concepts, Machine Learning, Deep Learning
Topic Covered: 
  2. - Welcome to this course and specialization
  3. - Starting a Jupyter Notebook
  4. - Creating variables in Python
  5. - Conditional statements and loops in Python
  6. - Creating functions and lambdas in Python
  7. - Starting Turi Create & loading an SFrame
  8. - Canvas for data visualization
  9. - Interacting with columns of an SFrame
  10. - Using .apply() for data transformation
  11. - Predicting house prices: A case study in regression
  12. - What is the goal and how might you naively address it?
  13. - Linear Regression: A Model-Based Approach
  14. - Adding higher order effects
  15. - Evaluating overfitting via training/test split
  16. - Training/test curves
  17. - Adding other features
  18. - Other regression examples
  19. - Regression ML block diagram
  20. - Loading & exploring house sale data
  21. - Splitting the data into training and test sets
  22. - Learning a simple regression model to predict house prices from house size
  23. - Evaluating error (RMSE) of the simple model
  24. - Visualizing predictions of simple model with Matplotlib
  25. - Inspecting the model coefficients learned
  26. - Exploring other features of the data
  27. - Linear classifiers
  28. - Decision boundaries
  29. - Training and evaluating a classifier
  30. - What's a good accuracy?
  31. - False positives, false negatives, and confusion matrices
  32. - Learning curves
  33. - Class probabilities
  34. - Classification ML block diagram
  35. - Loading & exploring product review data
  36. - Creating the word count vector
  37. - Exploring the most popular product
  38. - Defining which reviews have positive or negative sentiment
  39. - Training a sentiment classifier
  40. - Evaluating a classifier & the ROC curve
  41. - Deep Learning

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Course Tags
Machine Learning Course Machine Learning Free Course Top Free Course Machine Learning Fundamentals Machine Learning With Python Machine Learning Concepts Deep Learning