Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Advanced Trading Algorithms
This course will provide back test results for all the strategies in developed and emerging markets. The learner will also be taught scientific ways of back testing without succumbing to either look ahead (or) survival bias. You will learn various methods of building a robust back testing system for the strategies discussed in the previous course. You will be taught how to differentiate between mere data mining and results based on solid empirical or theoretical foundation.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
Machine Learning With Big Data
Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Pointers, Arrays, and Recursion
The third course in the specialization Introduction to Programming in C introduces the programming constructs pointers, arrays, and recursion. Pointers provide control and flexibility when programming in C by giving you a way to refer to the location of other data. Arrays provide a way to bundle data by guaranteeing sequences of data are grouped together. Finally, recursive functions—functions that call themselves—provide an alternative to iteration that are very useful for implementing certain algorithms.
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Course by
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Self Paced
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21 ساعات
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الإنجليزية
Finding Hidden Messages in DNA (Bioinformatics I)
Named a top 50 MOOC of all time by Class Central! This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. In the first half of the course, we investigate DNA replication, and ask the question, where in the genome does DNA replication begin?
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Course by
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Self Paced
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16 ساعات
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الإنجليزية
Machine Learning: Classification
Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.
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Course by
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Self Paced
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21 ساعات
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الإنجليزية
Introduction to Business Analytics with R
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use a data analytic language, R, to efficiently prepare business data for analytic tools such as algorithms and visualizations.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Symmetric Cryptography
Welcome to Symmetric Cryptography! Symmetric cryptography relies on shared secret key to ensure message confidentiality, so that the unauthorized attackers cannot retrieve the message. The course describes substitution and transposition techniques, which were the bases for classical cryptography when the message is encoded in natural language such as English. Then, we build on product ciphers (using both substitution and transposition/permutation) to describe modern block ciphers and review the widely used cipher algorithms in DES, 3-DES, and AES.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision.
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Course by
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Self Paced
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27 ساعات
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الإنجليزية
Mathematical Thinking in Computer Science
Mathematical thinking is crucial in all areas of computer science: algorithms, bioinformatics, computer graphics, data science, machine learning, etc. In this course, we will learn the most important tools used in discrete mathematics: induction, recursion, logic, invariants, examples, optimality. We will use these tools to answer typical programming questions like: How can we be certain a solution exists? Am I sure my program computes the optimal answer?
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Course by
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Self Paced
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42 ساعات
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الإنجليزية
VLSI CAD Part I: Logic
A modern VLSI chip has a zillion parts -- logic, control, memory, interconnect, etc. How do we design these complex chips? Answer: CAD software tools. Learn how to build thesA modern VLSI chip is a remarkably complex beast: billions of transistors, millions of logic gates deployed for computation and control, big blocks of memory, embedded blocks of pre-designed functions designed by third parties (called “intellectual property” or IP blocks). How do people manage to design these complicated chips?
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
Algorithmic Toolbox
This online course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies.
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Course by
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Self Paced
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40 ساعات
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الإنجليزية
Ordered Data Structures
In this course, you will learn new data structures for efficiently storing and retrieving data that is structured in an ordered sequence. Such data includes an alphabetical list of names, a family tree, a calendar of events or an inventory organized by part numbers. The specific data structures covered by this course include arrays, linked lists, queues, stacks, trees, binary trees, AVL trees, B-trees and heaps. This course also shows, through algorithm complexity analysis, how these structures enable the fastest algorithms to search and sort data.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Machine Learning: Clustering & Retrieval
Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Algorithms on Strings
World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome. In this online course you will learn key pattern matching concepts: tries, suffix trees, suffix arrays and even the Burrows-Wheeler transform.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Cloud Computing Concepts, Part 1
Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems. Learn about such fundamental distributed computing "concepts" for cloud computing. Some of these concepts include: clouds, MapReduce, key-value/NoSQL stores, classical distributed algorithms, widely-used distributed algorithms, scalability, trending areas, and much, much more! Know how these systems work from the inside out.
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
Introduction to Google SEO
Unlock the secrets of SEO! Dive into the fascinating world of Search Engine Optimization (SEO) and discover how major search engines like Google rank websites and content. This introductory course is your gateway to mastering the art of SEO, offering a thrilling blend of theory and hands-on practice to boost your visibility on Google. Course Highlights: Demystifying SEO - Uncover the inner workings of Google, the world's most popular search engine. Gain insider knowledge on how search algorithms evaluate and rank content, giving you a competitive edge in the digital landscape.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Machine Learning: Regression
Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Google SEO Fundamentals
Gain an understanding of search engine algorithms and how they affect organic search results and websites. Building on this knowledge, you’ll learn the key elements for creating an effective SEO strategy, including how to select keywords and perform keyword research; consumer psychology and search behavior; and how to conduct on-page SEO analysis to identify opportunities to improve a website’s search optimization.
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Course by
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Self Paced
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30 ساعات
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الإنجليزية
Big Data Analysis with Scala and Spark
Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout.
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Course by
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Self Paced
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28 ساعات
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الإنجليزية
Algorithms on Graphs
If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks!
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Course by
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Self Paced
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55 ساعات
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الإنجليزية
Text Mining and Analytics
This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers.
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Course by
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Self Paced
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33 ساعات
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الإنجليزية
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.
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Course by
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18 ساعات
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الإنجليزية
Programming Fundamentals
Programming is an increasingly important skill, whether you aspire to a career in software development, or in other fields. This course is the first in the specialization Introduction to Programming in C, but its lessons extend to any language you might want to learn. This is because programming is fundamentally about figuring out how to solve a class of problems and writing the algorithm, a clear set of steps to solve any problem in its class. This course will introduce you to a powerful problem-solving process—the Seven Steps—which you can use to solve any programming problem.
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Fundamentals of Reinforcement Learning
Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning.
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Course by
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Self Paced
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15 ساعات
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الإنجليزية
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية