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Machine Learning Foundations: A Case Study Approach
  • Level Foundation
  • المدة 18 ساعات hours
  • الطبع بواسطة University of Washington
  • Offered by

عن

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? 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. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.

الوحدات

Why you should learn machine learning with us

    • 5 Videos
    • 3 Readings
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5 Videos

  • Welcome to this course and specialization
  • Who we are
  • Machine learning is changing the world
  • Why a case study approach?
  • Specialization overview

3 Readings

  • Important Update regarding the Machine Learning Specialization
  • Slides presented in this module
  • Get help and meet other learners. Join your Community!

Who this specialization is for and what you will be able to do

    • 5 Videos
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5 Videos

  • How we got into ML
  • Who is this specialization for?
  • What you'll be able to do
  • The capstone and an example intelligent application
  • The future of intelligent applications

Getting started with the tools for the course

    • 3 Readings
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3 Readings

  • Getting started with Python, Jupyter Notebook, & Turi Create
  • Where should my files go?
  • Important changes from previous courses

Getting started with Python and the Jupyter Notebook

    • 4 Videos
    • 1 Readings
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4 Videos

  • Starting a Jupyter Notebook
  • Creating variables in Python
  • Conditional statements and loops in Python
  • Creating functions and lambdas in Python

1 Readings

  • Download the Jupyter Notebook used in this lesson to follow along

Getting started with SFrames for data engineering and analysis

    • 4 Videos
    • 1 Readings
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4 Videos

  • Starting Turi Create & loading an SFrame
  • Canvas for data visualization
  • Interacting with columns of an SFrame
  • Using .apply() for data transformation

1 Readings

  • Download the Jupyter Notebook used in this lesson to follow along

More SFrames Practice

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • SFrames

1 Readings

  • Download Wiki People Data

Linear regression modeling

    • 4 Videos
    • 1 Readings
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4 Videos

  • Predicting house prices: A case study in regression
  • What is the goal and how might you naively address it?
  • Linear Regression: A Model-Based Approach
  • Adding higher order effects

1 Readings

  • Slides presented in this module

Evaluating regression models

    • 4 Videos
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4 Videos

  • Evaluating overfitting via training/test split
  • Training/test curves
  • Adding other features
  • Other regression examples

Summary of regression

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Regression

1 Videos

  • Regression ML block diagram

Predicting house prices: Jupyter Notebook

    • 10 Videos
    • 1 Readings
Show info about module content

10 Videos

  • Loading & exploring house sale data
  • Splitting the data into training and test sets
  • Learning a simple regression model to predict house prices from house size
  • Evaluating error (RMSE) of the simple model
  • Visualizing predictions of simple model with Matplotlib
  • Inspecting the model coefficients learned
  • Exploring other features of the data
  • Learning a model to predict house prices from more features
  • Applying learned models to predict price of an average house
  • Applying learned models to predict price of two fancy houses

1 Readings

  • Download the Jupyter Notebook used in this lesson to follow along

Programming assignment

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Predicting house prices

1 Readings

  • Predicting house prices assignment

Classification modeling

    • 5 Videos
    • 1 Readings
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5 Videos

  • Analyzing the sentiment of reviews: A case study in classification
  • What is an intelligent restaurant review system?
  • Examples of classification tasks
  • Linear classifiers
  • Decision boundaries

1 Readings

  • Slides presented in this module

Evaluating classification models

    • 5 Videos
Show info about module content

5 Videos

  • Training and evaluating a classifier
  • What's a good accuracy?
  • False positives, false negatives, and confusion matrices
  • Learning curves
  • Class probabilities

Summary of classification

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Classification

1 Videos

  • Classification ML block diagram

Analyzing sentiment: Jupyter Notebook

    • 8 Videos
    • 1 Readings
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8 Videos

  • Loading & exploring product review data
  • Creating the word count vector
  • Exploring the most popular product
  • Defining which reviews have positive or negative sentiment
  • Training a sentiment classifier
  • Evaluating a classifier & the ROC curve
  • Applying model to find most positive & negative reviews for a product
  • Exploring the most positive & negative aspects of a product

1 Readings

  • Download the Jupyter Notebook used in this lesson to follow along

Programming assignment

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Analyzing product sentiment

1 Readings

  • Analyzing product sentiment assignment

Algorithms for retrieval and measuring similarity of documents

    • 6 Videos
    • 1 Readings
Show info about module content

6 Videos

  • Document retrieval: A case study in clustering and measuring similarity
  • What is the document retrieval task?
  • Word count representation for measuring similarity
  • Prioritizing important words with tf-idf
  • Calculating tf-idf vectors
  • Retrieving similar documents using nearest neighbor search

1 Readings

  • Slides presented in this module

Clustering models and algorithms

    • 4 Videos
Show info about module content

4 Videos

  • Clustering documents task overview
  • Clustering documents: An unsupervised learning task
  • k-means: A clustering algorithm
  • Other examples of clustering

Summary of clustering and similarity

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Clustering and Similarity

1 Videos

  • Clustering and similarity ML block diagram

Document retrieval: Jupyter Notebook

    • 6 Videos
    • 1 Readings
Show info about module content

6 Videos

  • Loading & exploring Wikipedia data
  • Exploring word counts
  • Computing & exploring TF-IDFs
  • Computing distances between Wikipedia articles
  • Building & exploring a nearest neighbors model for Wikipedia articles
  • Examples of document retrieval in action

1 Readings

  • Download the Jupyter Notebook used in this lesson to follow along

Programming assignment

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Retrieving Wikipedia articles

1 Readings

  • Retrieving Wikipedia articles assignment

Recommender systems

    • 3 Videos
    • 1 Readings
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3 Videos

  • Recommender systems overview
  • Where we see recommender systems in action
  • Building a recommender system via classification

1 Readings

  • Slides presented in this module

Co-occurrence matrices for collaborative filtering

    • 3 Videos
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3 Videos

  • Collaborative filtering: People who bought this also bought...
  • Effect of popular items
  • Normalizing co-occurrence matrices and leveraging purchase histories

Matrix factorization

    • 5 Videos
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5 Videos

  • The matrix completion task
  • Recommendations from known user/item features
  • Predictions in matrix form
  • Discovering hidden structure by matrix factorization
  • Bringing it all together: Featurized matrix factorization

Performance metrics for recommender systems

    • 3 Videos
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3 Videos

  • A performance metric for recommender systems
  • Optimal recommenders
  • Precision-recall curves

Summary of recommender systems

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Recommender Systems

1 Videos

  • Recommender systems ML block diagram

Song recommender: Jupyter Notebook

    • 4 Videos
    • 1 Readings
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4 Videos

  • Loading and exploring song data
  • Creating & evaluating a popularity-based song recommender
  • Creating & evaluating a personalized song recommender
  • Using precision-recall to compare recommender models

1 Readings

  • Download the Jupyter Notebook used in this lesson to follow along

Programming assignment

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Recommending songs

1 Readings

  • Recommending songs assignment

Neural networks: Learning very non-linear features

    • 3 Videos
    • 1 Readings
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3 Videos

  • Searching for images: A case study in deep learning
  • What is a visual product recommender?
  • Learning very non-linear features with neural networks

1 Readings

  • Slides presented in this module

Deep learning & deep features

    • 6 Videos
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6 Videos

  • Application of deep learning to computer vision
  • Deep learning performance
  • Demo of deep learning model on ImageNet data
  • Other examples of deep learning in computer vision
  • Challenges of deep learning
  • Deep Features

Summary of deep learning

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Deep Learning

1 Videos

  • Deep learning ML block diagram

Deep features for image classification: Jupyter Notebook

    • 3 Videos
    • 1 Readings
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3 Videos

  • Loading image data
  • Training & evaluating a classifier using raw image pixels
  • Training & evaluating a classifier using deep features

1 Readings

  • Download the Jupyter Notebook used in this lesson to follow along

Deep features for image retrieval: Jupyter Notebook

    • 5 Videos
    • 1 Readings
Show info about module content

5 Videos

  • Loading image data
  • Creating a nearest neighbors model for image retrieval
  • Querying the nearest neighbors model to retrieve images
  • Querying for the most similar images for car image
  • Displaying other example image retrievals with a Python lambda

1 Readings

  • Download the Jupyter Notebook used in this lesson to follow along

Programming assignment

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Deep features for image retrieval

1 Readings

  • Deep features for image retrieval assignment

Deploying machine learning as a service

    • 3 Videos
    • 1 Readings
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3 Videos

  • You've made it!
  • Deploying an ML service
  • What happens after deployment?

1 Readings

  • Slides presented in this module

Machine learning challenges and future directions

    • 4 Videos
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4 Videos

  • Open challenges in ML
  • Where is ML going?
  • What's ahead in the specialization
  • Thank you!

Auto Summary

Unlock the potential of your data with "Machine Learning Foundations: A Case Study Approach" by Coursera. Dive into practical case studies to predict house prices, analyze sentiment, recommend products, and more. This foundational course, led by expert instructors, spans 1080 minutes and covers key machine learning tasks like regression, classification, and deep learning. Perfect for beginners and professionals, it offers hands-on Python implementation and multiple subscription options, including Starter, Professional, and Paid. Ideal for those eager to apply machine learning in diverse domains and build intelligent applications.

Emily Fox
Instructors

Emily Fox

Carlos Guestrin
Instructors

Carlos Guestrin