Machine Learning Foundations A Case Study Approach

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.

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Syllabus

Syllabus - What you will learn from this course
Week 1
Welcome
Week 2
Regression: Predicting House Prices
Week 3
Classification: Analyzing Sentiment
Week 4
Clustering and Similarity: Retrieving Documents
Week 5
Recommending Products
Week 6
Deep Learning: Searching for Images
Closing Remarks

FAQ

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Reviews

With a funny and welcoming look and feel, this course introduces machine learning through a hands-on approach, that enables the student to properly understand what ML is all about. Very nicely done!

Good for a introductory course if someone is getting started with machine learning, but as part of an specialization i think is useless (for people who are planning to take all the specialization).

Really liked the course and the teachers. Would have preferred more detail on the quizzes so I didn't feel as lost as I did some of the times while trying to piece together what a question meant.

it would be really great if you will teach the provided note book practice examples

and deep learning is a bit harder and faster

instead graphlab if you use sklearn module it would be amazing