Search result for Mit introduction to algorithms Online Courses & Certifications
Get Course Alerts by Email
Deep Learning and Reinforcement Learning
by Mark J Grover , Miguel Maldonado- 4.7
Approx. 14 hours to complete
Introduction to Neural Networks Introduction to Neural Networks - Part 1 Introduction to Neural Networks - Part 2 Introduction to Neural Networks - Part 3 Introduction to Neural Networks - Part 4 Introduction to Neural Networks Notebook - Part 1 Introduction to Neural Networks Notebook - Part 2 Introduction to Backpropagation in Neural Networks - Part 1...
Machine Learning for Data Science and Analytics
by Ansaf Salleb-Aouissi , Cliff Stein , David Blei , Itsik Peer- 0.0
5 Weeks
This data science course is an introduction to machine learning and algorithms. We will also examine why algorithms play an essential role in Big Data analysis. How machine learning uses computer algorithms to search for patterns in data Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming...
$99
Algorithms, Part I
by Kevin Wayne , Robert Sedgewick- 4.9
Approx. 54 hours to complete
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Course Introduction Course Introduction Welcome to Algorithms, Part I Analysis of Algorithms Analysis of Algorithms Introduction Theory of Algorithms Sorting Introduction...
Art and Science of Machine Learning
by Google Cloud Training- 4.6
Approx. 19 hours to complete
Welcome to the Art and Science of machine learning. We’ll cover some of the most common model optimization algorithms and show you how to specify an optimization method in your TensorFlow code. Introduction Lab Intro: Export data from BigQuery to Google Cloud Storage Introduction Introduction to Neural Networks Introduction to Embeddings...
Probability - The Science of Uncertainty and Data
by John Tsitsiklis , Dimitri Bertsekas , Patrick Jaillet , Karene Chu- 0.0
16 Weeks
an introduction to random processes (Poisson processes and Markov chains) The contents of this courseare heavily based upon the corresponding MIT class -- Introduction to Probability -- a course that has been offered and continuously refined over more than 50 years. Master the skills needed to be an informed and effective practitioner of data science....
$300
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
by Tim Roughgarden- 4.8
Approx. 15 hours to complete
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees). Introduction to Greedy Algorithms Application to Clustering Introduction and Motivation...
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...
Related searches
Intro to Algorithms
by Michael Littman- 0.0
Timeline Approx. 4 Months
This class will give you an introduction to the design and analysis of algorithms, enabling you to analyze networks and discover how individuals are connected. lesson 3 Basic Graph Algorithms Find the quickest route to Kevin Bacon. lesson 4 It's Who You Know Learn to keep track of your best friends using heaps....
Free
Modern C++ Concurrency in Depth ( C++17/20)
by Kasun Liyanage- 0.0
9.5 hours on-demand video
But I liked to categorized it under system programming language. C++ paradigm took sharp turn with the introduction of C++11 standards. But if we want to code thread safe code which can harvest underline processors true power is much more difficult task. Proper lock free implementations of data structures and algorithms will provide unprecedented performance output....
$15.99
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....