Overview
Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.
As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.
In this Machine Learning with Python - from Linear Models to Deep Learning certificate at Massachusetts Institute of Technology - MITx, students will learn about principles and algorithms for turning training data into effective automated predictions.
We will cover:
- Representation, over-fitting, regularization, generalization, VC dimension;
- Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
- On-line algorithms, support vector machines, and neural networks/deep learning.
Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.
This course is part of the MITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science.
What you'll learn
Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning
Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models
Choose suitable models for different applications
Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.
Get more details
Visit programme websiteProgramme Structure
Syllabus
Lectures :
- Linear classifiers, separability, perceptron algorithm
- Maximum margin hyperplane, loss, regularization
- Stochastic gradient descent, over-fitting, generalization
- Linear regression
- Recommender problems, collaborative filtering
- Non-linear classification, kernels
- Learning features, Neural networks
- Deep learning, back propagation
- Recurrent neural networks
- Generalization, complexity, VC-dimension
- Unsupervised learning: clustering
- Generative models, mixtures
- Mixtures and the EM algorithm
- Learning to control: Reinforcement learning
- Reinforcement learning continued
- Applications: Natural Language Processing
Projects :
- Automatic Review Analyzer
- Digit Recognition with Neural Networks
- Reinforcement Learning
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 4 months
- 10 hrs/week
Start dates & application deadlines
Language
Delivered
Disciplines
Software Engineering Machine Learning View 486 other Short Courses in Software Engineering in United StatesExplore more key information
Visit programme websiteAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Prerequisites:
- 6.00.1x or proficiency in Python programming
- 6.431x or equivalent probability theory course
- College-level single and multi-variable calculus
- Vectors and matrices
Make sure you meet all requirements
Visit programme websiteTuition Fee
-
International
300 USD/fullTuition FeeBased on the tuition of 300 USD for the full programme during 4 months. -
National
300 USD/fullTuition FeeBased on the tuition of 300 USD for the full programme during 4 months.
- Add a Verified Certificate for $300 USD
- Limited access:free