Search result for Mit introduction to algorithms lectures Online Courses & Certifications
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Machine Learning Algorithms: Supervised Learning Tip to Tail
by Anna Koop- 4.7
Approx. 9 hours to complete
Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. Introduction to the Course...
Machine Learning for All
by Dr Marco Gillies- 4.7
Approx. 22 hours to complete
You really need to understand this technology, and this course is a great place to get that understanding. Whoever you are, we are looking forward to guiding you through you first machine learning project. NB this course is designed to introduce you to Machine Learning without needing any programming. Introduction to Data Features Introduction to Machine Learning in practice...
Fundamentals of Reinforcement Learning
by Martha White , Adam White- 4.8
Approx. 15 hours to complete
This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. Welcome to the Course! An Introduction to Sequential Decision-Making...
Build Regression, Classification, and Clustering Models
by Anastas Stoyanovsky- 0.0
Approx. 20 hours to complete
In most cases, the ultimate goal of a machine learning project is to produce a model. Models are constructed using algorithms, and in the world of machine learning, there are many different algorithms to choose from. Ultimately, this course begins a technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models....
Analytical Solutions to Common Healthcare Problems
by Brian Paciotti- 4.4
Approx. 11 hours to complete
In this course, we’re going to go over analytical solutions to common healthcare problems. I will review these business problems and you’ll build out various data structures to organize your data. How to Make Fairer Comparisons Using Risk Adjustment Algorithms and "Groupers" Module 2 Introduction Clinical Identification Algorithms (CIA) Module 3 Introduction...
Genome Sequencing (Bioinformatics II)
by Pavel Pevzner , Phillip Compeau- 4.6
Approx. 17 hours to complete
We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Week 1: Introduction to Genome Sequencing Week 2: Applying Euler's Theorem to Assemble Genomes...
Introduction to the Discrete Fourier Transform with Python
by Long Nguyen- 4.4
2 hours on-demand video
The Discrete Fourier Transform (DFT) is one of the most useful algorithms in computer science and digital signal processing. This course is a very basic introduction to the Discrete Fourier Transform. gain a deeper appreciation for the DFT by applying it to simple applications using Python be able to filter out noise from a sound file using Python...
$11.99
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Introduction to Business Analytics with R
by Ronald Guymon , Ashish Khandelwal- 4.5
Approx. 16 hours to complete
In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Course Introduction Introduction to Business Analytics and R Introduction to R Module 2 Introduction Getting to Know Your Data 1: Explore as in Excel Introduction to Other Data Types Review of Notebooks and Introduction to dplyr...
Machine Learning with Python
by SAEED AGHABOZORGI , Joseph Santarcangelo- 4.7
Approx. 21 hours to complete
First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Introduction to Machine Learning Introduction to Machine Learning Intro to Machine Learning Introduction to Regression Introduction to Classification Introduction to Decision Trees Intro to Logistic Regression Intro to Clustering Intro to k-Means...
Artificial Intelligence Data Fairness and Bias
by Brent Summers- 4.9
Approx. 6 hours to complete
As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models. Course Introduction Video Algorithms inside of algorithms: Getting to fair...