Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
Our Courses
Developing AI Applications on Azure
This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning. Next, this course introduces the machine learning tools available in Microsoft Azure. We'll review standardized approaches to data analytics and you'll receive specific guidance on Microsoft's Team Data Science Approach.
-
Course by
-
Self Paced
-
16 hours
-
English
Compare Models with Experiments in Azure ML Studio
Did you know that you can compare models in Azure Machine Learning? In this 1-hour project-based course, you will learn how to log plots in experiments, log numeric metrics in experiments and visualize metrics in Azure Machine Learning Studio. To achieve this, we will use one example data, train a couple of machine learning algorithms in Jupyter notebook and visualize their results in Azure Machine Learning Studio Portal interface. In order to be successful in this project, you will need knowledge of Python language and experience with machine learning in Python.
-
Course by
-
Self Paced
-
4 hours
-
English
Data Engineering in AWS
Data Engineering in AWS is the first course in the AWS Certified Machine Learning Specialty specialization. This course helps learners to analyze various data gathering techniques. They will also gain insight to handle missing data. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:30-3:00 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.
-
Course by
-
Self Paced
-
4 hours
-
English
Genomic Data Science and Clustering (Bioinformatics V)
How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world?
-
Course by
-
Self Paced
-
10 hours
-
English
Predict Ad Clicks Using Logistic Regression and XG-Boost
In this project, we will predict Ads clicks using logistic regression and XG-boost algorithms. In this project, we will assume that you have been hired as a consultant to a start-up that is running a targeted marketing ad campaign on Facebook. The company wants to analyze customer behavior by predicting which customer clicks on the advertisement.
-
Course by
-
Self Paced
-
3 hours
-
English
Machine Translation
Welcome to the CLICS-Machine Translation MOOC This MOOC explains the basic principles of machine translation. Machine translation is the task of translating from one natural language to another natural language. Therefore, these algorithms can help people communicate in different languages. Such algorithms are used in common applications, from Google Translate to apps on your mobile device. After taking this course you will be able to understand the main difficulties of translating natural languages and the principles of different machine translation approaches.
-
Course by
-
Self Paced
-
28 hours
-
English
Introduction to Machine Learning in Sports Analytics
In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes.
-
Course by
-
Self Paced
-
13 hours
-
English
Parallel programming (Scala 2 version)
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm.
-
Course by
-
Self Paced
-
33 hours
-
English
Automated Reasoning: Symbolic Model Checking
The Automated Reasoning: Symbolic Model Checking course presents how the properties of acting systems and programs can be verified automatically. The basic notion is a transition system: any system that can be described by states and steps. We present how in CTL (computation tree logic) properties like reach-ability can be described. Typically, a state space may be very large. One way to deal with this is symbolic model checking: a way in which sets of states are represented symbolically.
-
Course by
-
Self Paced
-
13 hours
-
English
Methods for Solving Problems
In this course, we will explore different types of problems, the boundaries of what makes a problem solvable, and the different algorithms, rules, and heuristics we can implement to solve these problems.
-
Course by
-
Self Paced
-
7 hours
-
English
Approximation Algorithms
Many real-world algorithmic problems cannot be solved efficiently using traditional algorithmic tools, for example, because the problems are NP-hard. The goal of the Approximation Algorithms course is to become familiar with important algorithmic concepts and techniques needed to effectively deal with such problems. These techniques apply when we don't require the optimal solution to certain problems, but an approximation that is close to the optimal solution. We will see how to efficiently find such approximations.
-
Course by
-
Self Paced
-
15 hours
-
English
Digital Signal Processing 4: Applications
Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up.
-
Course by
-
Self Paced
-
14 hours
-
English
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform.
-
Course by
-
Self Paced
-
38 hours
-
English
Search Engines for Web and Enterprise Data
This course introduces the technologies behind web and search engines, including document indexing, searching and ranking. You will also learn different performance metrics for evaluating search quality, methods for understanding user intent and document semantics, and advanced applications including recommendation systems and summarization. Real-life examples and case studies are provided to reinforce the understanding of search algorithms.
-
Course by
-
Self Paced
-
30 hours
-
English
Digital Signal Processing 2: Filtering
Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up.
-
Course by
-
Self Paced
-
18 hours
-
English
Clinical Natural Language Processing
This course teaches you the fundamentals of clinical natural language processing (NLP). In this course you will learn the basic linguistic principals underlying NLP, as well as how to write regular expressions and handle text data in R. You will also learn practical techniques for text processing to be able to extract information from clinical notes. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop text processing algorithms to identify diabetic complications from clinical notes.
-
Course by
-
Self Paced
-
13 hours
-
English
Business Analytics Executive Overview
Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. This course will focus on understanding key analytics concepts and the breadth of analytic possibilities.
-
Course by
-
Self Paced
-
17 hours
-
English
The Economics of AI
The course introduces you to cutting-edge research in the economics of AI and the implications for economic growth and labor markets. We start by analyzing the nature of intelligence and information theory. Then we connect our analysis to modeling production and technological change in economics, and how these processes are affected by AI. Next we turn to how technological change drives aggregate economic growth, covering a range of scenarios including a potential growth singularity.
-
Course by
-
Self Paced
-
28 hours
-
English
C for Everyone: Structured Programming
In the new world we live in, coding is a universally valuable skill, whether you're a scientist, artist, or a humanist. Algorithms are everywhere, and we all have to understand how they work. The C language is particularly well suited as an introduction to coding: It's a tried-and-true language, and it allows you to understand computing processes at a deep level. You will also get an introduction to more advanced programming in C++ in the final module. This is a continuation of C for Everyone: Fundamentals, which you should take before beginning this course.
-
Course by
-
Self Paced
-
10 hours
-
English
Software Security
This course we will explore the foundations of software security. We will consider important software vulnerabilities and attacks that exploit them -- such as buffer overflows, SQL injection, and session hijacking -- and we will consider defenses that prevent or mitigate these attacks, including advanced testing and program analysis techniques. Importantly, we take a "build security in" mentality, considering techniques at each phase of the development cycle that can be used to strengthen the security of software systems.
-
Course by
-
Self Paced
-
18 hours
-
English
Data-driven Astronomy
Science is undergoing a data explosion, and astronomy is leading the way. Modern telescopes produce terabytes of data per observation, and the simulations required to model our observable Universe push supercomputers to their limits. To analyse this data scientists need to be able to think computationally to solve problems. In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools.
-
Course by
-
Self Paced
-
24 hours
-
English
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the DeepLearning.AI TensorFlow Developer Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning.
-
Course by
-
Self Paced
-
18 hours
-
English
Algorithms, Data Collection, and Starting to Code
This course starts you on your journey learning about computational thinking and beginning C programming. If you’d like to explore how we can interact with the world in a rigorous, computational way, and would also like to start learning to program, this is the course for you! You may have heard lots of talk about computational thinking recently, but if you ask 10 different people what it is you’ll probably get 10 different answers. Rather than trying to define computational thinking, we’ll just say it’s a problem-solving process that includes lots of different components.
-
Course by
-
Self Paced
-
15 hours
-
English
IT Security: Defense against the digital dark arts
This course covers a wide variety of IT security concepts, tools, and best practices. It introduces threats and attacks and the many ways they can show up. We’ll give you some background of encryption algorithms and how they’re used to safeguard data. Then, we’ll dive into the three As of information security: authentication, authorization, and accounting. We’ll also cover network security solutions, ranging from firewalls to Wifi encryption options.
-
Course by
-
Self Paced
-
32 hours
-
English
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.
-
Course by
-
Self Paced
-
24 hours
-
English