This course can be found at Coursera

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.

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.

• Week 1 - Introduction

  • Why you should learn machine learning with us
  • Who this specialization is for and what you will be able to do
  • Getting started with Python, Jupyter Notebook & Turi Create
  • Getting started with SFrames for data engineering and analysis
  • Week 1 Exercises:
    
    • Course examples
    • Getting started with SFrames assignment

• Week 2 - Regression

  • Linear regression modeling
  • Evaluating regression models
  • Summary of regression
  • Week 2 Exercises:
    
    • Course examples - Predicting House Prices
    • Predicting House Prices assignment

• Week 3 Classification: Analyzing Sentiment

  • Classification modeling
  • Evaluating classification models
  • Summary of classification
  • Week 3 Exercises:
    
    • Course examples - Analyzing sentiment
    • Analyzing sentiment assignment

• Week 4 - Clustering and Similarity: Retrieving Documents

  • Algorithms for retrieval and measuring similarity of documents
  • Clustering models and algorithms
  • Summary of clustering and similarity
  • Week 4 Exercises:
    
    • Course examples - Document retrieval
    • Retrieving Wikipedia articles assignment

• Week 5 - Recommender systems

  • About Recommender systems
  • Co-occurrence matrices for collaborative filtering
  • Matrix factorization
  • Performance metrics for recommender systems
  • Summary of recommender systems
  • Week 5 Exercises:
    
    • Course examples - Song recommender system
    • Recommending Songs assignment

• Week 6 - Deep Learning

  • Neural networks: Learning very non-linear features
  • Deep learning & deep features
  • Summary of deep learning
  • Week 6 Exercises:
    
    • Course examples - Deep features for image classification
    • Deep features for image retrieval assignment