Course: Machine Learning Foundations: A Case Study Approach
Modules:
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
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
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
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
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
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
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!