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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

Machine Learning: Regression is a certification programme offered completely in the online mode. In the course by the University of Washington, the students will be exposed to machine learning, particularly regression. Machine Learning: Regression Certification Syllabus will talk about regression which is predictive modelling in machine learning that will help to foresee the outcomes analyzing the algorithm.

In the Machine Learning: Regression Certification Course, offered by Coursera, the candidates will learn the regression based on the case study of the ‘Predicting Housing Prices’ where the students will come up with a model that will predict a continuous value/price based on the input features such as a number of bathrooms, bedrooms, etc. Machine Learning: Regression Certification by Coursera is the second in the four courses of Machine Learning Specialization.

The Highlights

Provided by Coursera
Approximately 22 hours of programme
Offered by the University of Washington
Flexible Deadlines
Self-Paced Learning Option
Shareable Certificate
Financial Aid Available
100% Online Course

Programme Offerings

English videos with multiple subtitles
practice quizzes
Graded Assignments with peer feedback
graded Quizzes with feedback
Graded Programming Assignments
Course Videos & Readings
EMI payment options
14 day refund period

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

Machine Learning: The regression Certification Fee is determined by how many months the students want to dedicate to the programme. Refer to the table below to know the month-wise fee structure:

Description	Total Fee in INR
Course Fee, 1 month	Rs. 4,047
Course Fee, 3 months	Rs. 8,149
Course Fee, 6 months	Rs. 12,224

Eligibility Criteria

Certification Qualifying Details

To get awarded the Machine Learning: Regression Certification, the students will need to complete the whole process of the programme including the course materials, quizzes, and assignments.

What you will learn

Machine learning

By the end of the Machine Learning: Regression Training, the students will learn the following concepts:

Linear Regression
Ridge Regression
Lasso (Statistics)
Regression Analysis
Input and output of a regression model.

Who it is for

Data ScientistBusiness AnalystBig Data Analytics EngineerAnalytics manager

Machine Learning: Regression Classes are a better option for the following professionals:

Data Scientist
Big Data Analyst
Analytics Manager
Business Analyst
Developer

Admission Details

Step 1 - At first, the learners must register and sign up on https://www.coursera.org/ to get access to the programmes offered by Coursera.

Step 2 - After activating the Coursera account, the candidate can sign in.

Step 3 - Then, the candidate can search the ‘University of Washington’ in the search column, and then, the programmes provided by the University of Washington will be shown on the screen.

Step 4 - Then, find the course 'Machine Learning: Regression’ in the list and click on it.

Step 5- Then, the page of the course will appear on the screen, and then, click on the option ‘enroll’. They enroll in the programme either by the option of ‘Audit Only’ or ‘Purchase Course’.

The Syllabus

Videos

Welcome!
What is the course about?
Outlining the first half of the course
Outlining the second half of the course
Assumed background

Readings

Important Update regarding the Machine Learning Specialization
Slides presented in this module
Reading: Software tools you'll need

Videos

A case study in predicting house prices
Regression fundamentals: data & model
Regression fundamentals: the task
Regression ML block diagram
The simple linear regression model
The cost of using a given line
Using the fitted line
Interpreting the fitted line
Defining our least squares optimization objective
Finding maxima or minima analytically
Maximizing a 1d function: a worked example
Finding the max via hill climbing
Finding the min via hill descent
Choosing step size and convergence criteria
Gradients: derivatives in multiple dimensions
Gradient descent: multidimensional hill descent
Computing the gradient of RSS
Approach 1: closed-form solution
Approach 2: gradient descent
Comparing the approaches
Influence of high leverage points: exploring the data4m
Influence of high leverage points: removing Center City
Influence of high leverage points: removing high-end towns
Asymmetric cost functions
A brief recap

Readings

Slides presented in this module
Optional reading: worked-out example for closed-form solution
Optional reading: worked-out example for gradient descent
Download notebooks to follow along
Fitting a simple linear regression model on housing data

Quizzes

Simple Linear Regression
Fitting a simple linear regression model on housing data

Video

Multiple regression intro
Polynomial regression
Modeling seasonality
Where we see seasonality
Regression with general features of 1 input
Motivating the use of multiple inputs
Defining notation
Regression with features of multiple inputs
Interpreting the multiple regression fit
Rewriting the single observation model in vector notation
Rewriting the model for all observations in matrix notation
Computing the cost of a D-dimensional curve
Computing the gradient of RSS
Approach 1: closed-form solution
Discussing the closed-form solution
Approach 2: gradient descent
Feature-by-feature update
Algorithmic summary of gradient descent approach
A brief recap

Readings

Slides presented in this module
Optional reading: a review of matrix algebra
Exploring different multiple regression models for house price prediction
Numpy tutorial
Implementing gradient descent for multiple regression

Quizzes

Multiple Regression
Exploring different multiple regression models for house price prediction
Implementing gradient descent for multiple regression

Videos

Assessing performance intro
What do we mean by "loss"?
Training error: assessing loss on the training set
Generalization error: what we really want
Test error: what we can actually compute
Defining overfitting
Training/test split
Irreducible error and bias
Variance and the bias-variance tradeoff
Error vs. amount of data
Formally defining the 3 sources of error
Formally deriving why 3 sources of error
Training/validation/test split for model selection, fitting, and assessment
A brief recap

Readings

Slides presented in this module
Polynomial Regression

Quizzes

Assessing Performance
Exploring the bias-variance tradeoff

Videos

Symptoms of overfitting in polynomial regression
Overfitting demo
Overfitting for more general multiple regression models
Balancing fit and magnitude of coefficients
The resulting ridge objective and its extreme solutions
How ridge regression balances bias and variance
Ridge regression demo
The ridge coefficient path
Computing the gradient of the ridge objective
Approach 1: closed-form solution
Discussing the closed-form solution
Approach 2: gradient descent
Selecting tuning parameters via cross validation
K-fold cross-validation
How to handle the intercept
A brief recap

Readings

Slides presented in this module
Download the notebook and follow along
Download the notebook and follow along
Observing effects of L2 penalty in polynomial regression
Implementing ridge regression via gradient descent

Quizzes

Ridge Regression
Observing effects of L2 penalty in polynomial regression
Implementing ridge regression via gradient descent

Videos

The feature selection task
All subsets
Complexity of all subsets
Greedy algorithms
Complexity of the greedy forward stepwise algorithm
Can we use regularization for feature selection?
Thresholding ridge coefficients?
The lasso objective and its coefficient path
Visualizing the ridge cost
Visualizing the ridge solution
Visualizing the lasso cost and solution
Lasso demo
What makes the lasso objective different
Coordinate descent
Normalizing features
Coordinate descent for least squares regression (normalized features)
Coordinate descent for lasso (normalized features)
Assessing convergence and other lasso solvers
Coordinate descent for lasso (unnormalized features)
Deriving the lasso coordinate descent update
Choosing the penalty strength and other practical issues with a lasso
A brief recap

Readings

Slides presented in this module
Download the notebook and follow along
Using LASSO to select features
Implementing LASSO using coordinate descent

Quizzes

Feature Selection and Lasso
Using LASSO to select features
Implementing LASSO using coordinate descent

Videos

Limitations of parametric regression
1-Nearest neighbor regression approach
Distance metrics
1-Nearest neighbor algorithm
k-Nearest neighbors regression
k-Nearest neighbors in practice
Weighted k-nearest neighbors
From weighted k-NN to kernel regression
Global fits of parametric models vs. local fits of kernel regression
Performance of NN as the amount of data grows
Issues with high dimensions, data scarcity, and computational complexity
k-NN for classification
A brief recap

Readings

Slides presented in this module
Predicting house prices using k-nearest neighbors regression

Quizzes

Nearest Neighbors & Kernel Regression
Predicting house prices using k-nearest neighbors regression

Videos

Simple and multiple regression
Assessing performance and ridge regression
Feature selection, lasso, and nearest neighbor regression
What we covered and what we didn't cover
Thank you!

Readings

Slides presented in this module

Instructors

UW Washington Frequently Asked Questions (FAQ's)

1: What is the completion time of the Machine Learning: Regression Online Certification?

The course can be completed within about 22 hours.

2: Will the students be conferred with a certificate after the Machine Learning: Regression Online Course?

Yes, the students will be conferred with a shareable certificate after the completion of the programme.

3: In which languages the subtitles are available for the course videos?

The subtitles are available in the languages of Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, and Spanish.

4: Which institution is offering the programme?

The programme is offered by the University of Washington.

5: Would Coursera provide placement support?

No, Coursera will not render the placement support to the students.