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Machine Learning Foundations: A Case Study Approach

Approx. 18 hours to complete

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Course Summary

This course provides an introduction to machine learning concepts and techniques with practical applications using Python. Students will learn about supervised and unsupervised learning, as well as regression, classification, clustering, and dimensionality reduction.

Key Learning Points

Learn the basics of machine learning with Python
Understand supervised and unsupervised learning techniques
Apply machine learning concepts to real-world problems

Learning Outcomes

Understand machine learning concepts and techniques
Apply machine learning algorithms to real-world problems
Implement machine learning models using Python

Prerequisites or good to have knowledge before taking this course

Basic programming knowledge in Python
Familiarity with linear algebra and probability theory

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures
Hands-on projects

Similar Courses

Applied Data Science with Python
Machine Learning
Data Mining

Related Education Paths

Related Books

Description

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.

Outline

Welcome
Welcome to this course and specialization
Who we are
Machine learning is changing the world
Why a case study approach?
Specialization overview
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
Starting a Jupyter Notebook
Creating variables in Python
Conditional statements and loops in Python
Creating functions and lambdas in Python
Starting Turi Create & loading an SFrame
Canvas for data visualization
Interacting with columns of an SFrame
Using .apply() for data transformation
Important Update regarding the Machine Learning Specialization
Slides presented in this module
Getting started with Python, Jupyter Notebook, & Turi Create
Where should my files go?
Important changes from previous courses
Download the Jupyter Notebook used in this lesson to follow along
Download the Jupyter Notebook used in this lesson to follow along
Download Wiki People Data
SFrames

Regression: Predicting House Prices
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
Evaluating overfitting via training/test split
Training/test curves
Adding other features
Other regression examples
Regression ML block diagram
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
Slides presented in this module
Download the Jupyter Notebook used in this lesson to follow along
Predicting house prices assignment
Regression
Predicting house prices

Classification: Analyzing Sentiment
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
Training and evaluating a classifier
What's a good accuracy?
False positives, false negatives, and confusion matrices
Learning curves
Class probabilities
Classification ML block diagram
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
Slides presented in this module
Download the Jupyter Notebook used in this lesson to follow along
Analyzing product sentiment assignment
Classification
Analyzing product sentiment

Clustering and Similarity: Retrieving Documents
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
Clustering documents task overview
Clustering documents: An unsupervised learning task
k-means: A clustering algorithm
Other examples of clustering
Clustering and similarity ML block diagram
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
Slides presented in this module
Download the Jupyter Notebook used in this lesson to follow along
Retrieving Wikipedia articles assignment
Clustering and Similarity
Retrieving Wikipedia articles

Recommending Products
Recommender systems overview
Where we see recommender systems in action
Building a recommender system via classification
Collaborative filtering: People who bought this also bought...
Effect of popular items
Normalizing co-occurrence matrices and leveraging purchase histories
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
A performance metric for recommender systems
Optimal recommenders
Precision-recall curves
Recommender systems ML block diagram
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
Slides presented in this module
Download the Jupyter Notebook used in this lesson to follow along
Recommending songs assignment
Recommender Systems
Recommending songs

Deep Learning: Searching for Images
Searching for images: A case study in deep learning
What is a visual product recommender?
Learning very non-linear features with neural networks
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
Deep learning ML block diagram
Loading image data
Training & evaluating a classifier using raw image pixels
Training & evaluating a classifier using deep features
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
Slides presented in this module
Download the Jupyter Notebook used in this lesson to follow along
Download the Jupyter Notebook used in this lesson to follow along
Deep features for image retrieval assignment
Deep Learning
Deep features for image retrieval

Closing Remarks
You've made it!
Deploying an ML service
What happens after deployment?
Open challenges in ML
Where is ML going?
What's ahead in the specialization
Thank you!
Slides presented in this module

Summary of User Reviews

ML Foundations course on Coursera has received positive reviews from users. The course has been praised for its comprehensive curriculum, experienced instructors, and practical assignments. Many users found the course to be a great introduction to machine learning without being too overwhelming.

Key Aspect Users Liked About This Course

The course's comprehensive curriculum is a key aspect that many users found to be good.

Pros from User Reviews

Experienced instructors provide in-depth explanations of concepts
Practical assignments help reinforce learning
Good introduction to machine learning without being too overwhelming
Flexible schedule allows for self-paced learning

Cons from User Reviews

Some users found the course to be too basic
Limited interaction with instructors and other students
Lack of real-world applications
Not suitable for those looking for advanced machine learning concepts
No certificate of completion for the free version of the course

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