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Quick Facts

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

Course Overview

With machine learning and artificial intelligence gaining immense popularity, the skills of future-ready analysts, engineers, technologists, and data managers also must expand, shift, and advance. Machine Learning: Fundamentals and Algorithms online course will provide you with ample technical knowledge and analytical methods to prepare you for the next gen of innovation.

Organised around 10 modules, the Machine Learning: Fundamentals and Algorithms course help you deepen and broaden your Python programming skills for ML applications. You can further apply this technical knowledge to the industry verticals currently integrating machine learning and AI into their digital drivers.

The Machine Learning: Fundamentals and Algorithms programme is designed for participants with Python programming experience who want to learn more about the underlying mathematics. Upon programme completion, you will receive a verified digital certificate from Carnegie Mellon University.

The Highlights

Python Coding Exercise in Each Module
Dedicated Programme Support Team
Bite-Sized Learning
Peer Discussions
Mobile Learning App
Knowledge Checks
Offered by Carnegie Mellon University
10-week online programme

Programme Offerings

Bite-sized Learning
10-week online course
Coding exercises
Mobile Learning App
Support team
Peer discussions
Knowledge checks

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	CMU School of Computer Science, Pittsburgh

Paying the Machine Learning: Fundamentals and Algorithms course fee is mandatory.
A referral discount is available.
A 10% group discount is also available.

Machine Learning: Fundamentals and Algorithms fee structure

Course	Fees in USD
Machine Learning: Fundamentals and Algorithms	$2,500

Eligibility Criteria

To enrol in the Machine Learning: Fundamentals and Algorithms training, you must possess high-school-level proficiency in linear algebra, probability, calculus, statistics, and Python programming.

A certificate of completion will be offered when you complete the course.

What you will learn

Machine learning

By the end of the Machine Learning Fundamentals and Algorithms syllabus, you will be able to:

Synthesise components of machine learning to build functional tools for the prediction of unseen data
Apply concepts from statistics, linear algebra, probability, optimisation, and calculus to refine and describe the workings of machine learning algorithms
Analyse and implement learning algorithms for regression, classification, and clustering

Who it is for

Automation Test Engineer

The Machine Learning: Fundamentals and Algorithms course are ideal for:

Engineers - Design Engineers, Automation Engineers, Software Developers, Software Engineers
Data Analytics Professionals - Data Scientists, Data Analysts, Analysts, Business Analysts
Technical Managers/Directors - Directors of Business Systems & Information Technology, VP Analytics, Technology Director, Senior Engineer, Senior Developers, Tech Leads, VP Engineering, Director of Customer Experience, and many more

Admission Details

To commence the enrolment procedure for the Machine Learning: Fundamentals and Algorithms programme, visit the programme website - https://execonline.cs.cmu.edu/machine-learning
Once you’re on the website, hit the “Apply Now” button on the top.
Log in using your credentials, or create a new account.
Fill and submit the application form.
Complete the fee transaction.
Begin the course.

Application Details

During enrolment into the Machine Learning: Fundamentals and Algorithms programme, you have to fill an application form, which has fields like street address, full name, city, ZIP code, work experience, state, job title, company, profile, and industry.

The Syllabus

Use a decision tree to make predictions
Given labeled training examples, you will learn a decision tree.

In machine learning, there are fundamental algorithms.
Use the k-NN algorithm to classify points given a simple dataset
Implement a full decision tree for learning and prediction.

Employ model selection techniques to select k for the k-NN algorithm
Implement a grid search to select multiple hyperparameters for a model.

Creating machine learning solutions can require refinement of the inner workings of algorithms, including
- Adapting the k-NN algorithm for classification to regression
- Adapting decision trees for classification to regression, as well as
- Implementing learning for linear regression using gradient descent.

Determine how convexity affects optimization
Implement linear regression with optimization by stochastic gradient descent.

Given i.i.d. data and parameters of a logistic regression distribution, compute conditional likelihood
Implement stochastic gradient descent for binary logistic regression.

Ways to combat overfitting
Convert a nonlinear dataset to a linear dataset in higher dimensions
Manipulate the hyperparameters of L1 and L2 regularization implementations
Identify the effects on magnitude and sparsity of parameters.

Combine simpler models as components to build up feed-forward neural network architectures
Write mathematical expressions in scalar form defining a feed-forward neural network.