Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
Our Courses
离散优化算法篇 Solving Algorithms for Discrete Optimization
优化是决策的一般形式，在我们的社会中很常见。它的应用包括从求解数独谜题到婚礼上的座次安排。同样的技术可以调度航班和机组成员，协调钢铁生产，组织铁矿从矿井到港口的运输。人力资源和材料管理上好的决策可以令企业提升上百万的盈利。同样的问题很多也在我们日常生活出现，成为决定每天送货路线，决定学校时间表，传输电力到家里等种种问题的一部分。除了它们的重要性，这些问题如果用传统的本科计算机科学方法难以求解。 这门课程为已经完成离散优化高阶篇的同学设计。 请看这门课程的宣传视频：http://www.cpr.cuhk.edu.hk/cutv/detail/988
-
Course by
-
Self Paced
-
Chinese
XG-Boost 101: Used Cars Price Prediction
In this hands-on project, we will train 3 Machine Learning algorithms namely Multiple Linear Regression, Random Forest Regression, and XG-Boost to predict used cars prices.
-
Course by
-
Self Paced
-
3 hours
-
English
VLSI CAD Part II: Layout
You should complete the VLSI CAD Part I: Logic course before beginning this course. A 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? Answer: a sequence of computer aided design (CAD) tools takes an abstract description of the chip, and refines it step-wise to a final design.
-
Course by
-
Self Paced
-
24 hours
-
English
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?
-
Course by
-
Self Paced
-
23 hours
-
English
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 274.99 + VAT
Buy Now
Visualizing Filters of a CNN using TensorFlow
In this short, 1 hour long guided project, we will use a Convolutional Neural Network - the popular VGG16 model, and we will visualize various filters from different layers of the CNN. We will do this by using gradient ascent to visualize images that maximally activate specific filters from different layers of the model. We will be using TensorFlow as our machine learning framework.
-
Course by
-
Self Paced
-
1 hour
-
English
Visual Perception for Self-Driving Cars
Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the end of this course, you will be able to work with the pinhole camera model, perform intrinsic and extrinsic camera calibration, detect, describe and match image features and design your own convolutional neural networks.
-
Course by
-
31 hours
-
English
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 170.99 + VAT
Buy Now
Using Efficient Sorting Algorithms in Java to Arrange Tax Data
By the end of this project, you will learn how to create an application that sorts Missouri Tax Data into ascending order using a variety of critical sorting algorithms.
-
Course by
-
Self Paced
-
3 hours
-
English
Unsupervised Text Classification for Marketing Analytics
Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. This course uses Jupyter Notebooks and the coding environment Google Colab, a browser-based Jupyter notebook environment.
-
Course by
-
Self Paced
-
13 hours
-
English
Unsupervised Machine Learning for Customer Market Segmentation
In this hands-on guided project, we will train unsupervised machine learning algorithms to perform customer market segmentation.
-
Course by
-
Self Paced
-
3 hours
-
English
Unsupervised Machine Learning
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data.
-
Course by
-
Self Paced
-
23 hours
-
English
Unsupervised Learning and Its Applications in Marketing
Welcome to the Unsupervised Learning and Its Applications in Marketing course! In this course, you will delve into the fascinating world of unsupervised machine learning and its relevance to the field of marketing. Unsupervised learning is a powerful approach that allows us to uncover hidden patterns and insights from vast amounts of historical data without the need for explicit labels or human intervention.
-
Course by
-
Self Paced
-
22 hours
-
English
Unsupervised Algorithms in Machine Learning
One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.
-
Course by
-
Self Paced
-
38 hours
-
English
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary.
-
Course by
-
Self Paced
-
21 hours
-
English
Tweet Emotion Recognition with TensorFlow
In this 2-hour long guided project, we are going to create a recurrent neural network and train it on a tweet emotion dataset to learn to recognize emotions in tweets. The dataset has thousands of tweets each classified in one of 6 emotions. This is a multi class classification problem in the natural language processing domain. We will be using TensorFlow as our machine learning framework. You will need prior programming experience in Python.
-
Course by
-
Self Paced
-
3 hours
-
English
Train Machine Learning Models
This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems. To be successful in this course a learner should have a background in computing technology, including some aptitude in computer programming.
-
Course by
-
Self Paced
-
29 hours
-
English
Trading Algorithms
This course covers two of the seven trading strategies that work in emerging markets. The seven include strategies based on momentum, momentum crashes, price reversal, persistence of earnings, quality of earnings, underlying business growth, behavioral biases and textual analysis of business reports about the company. In the first part of the course, you will learn how to read an academic paper. What parts to pay attention to and what parts to skim through will be discussed here.
-
Course by
-
Self Paced
-
13 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
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 170.99 + VAT
Buy Now
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.
-
Course by
-
Self Paced
-
33 hours
-
English
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 274.99 + VAT
Buy Now
TensorFlow for Beginners: Basic Binary Image Classification
The goal of this project is to introduce beginners to the basic concepts of machine learning using TensorFlow. The project will include, how to set up the tool and get started as well as understanding the fundamentals of machine learning/neural network model and its key concepts. Learning how to use TensorFlow for implementing machine learning algorithms, data preprocessing, supervised learning. Additionally, learners develop skills in evaluating and deploying machine learning models using TensorFlow.
-
Course by
-
Self Paced
-
4 hours
-
English
Teaching Impacts of Technology: Relationships
In this course you’ll focus on how “smart” devices have changed how we interact with others in personal ways, impacting how we stay connected in our increasingly mobile society. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level.
-
Course by
-
Self Paced
-
11 hours
-
English
Teaching Impacts of Technology: Fundamentals
In this course you’ll focus on the fundamentals of teaching the impacts of technology, starting by exploring how you interact with and benefit from technology in a typical 24 hour period, such as the desire for instant food and entertainment. This will be done through a series of paired teaching sections, exploring a specific “Impact of Computing” in your typical day and the “Technologies and Computing Concepts” that enable that impact, all at a K12-appropriate level.
-
Course by
-
Self Paced
-
11 hours
-
English
System Validation (2): Model process behaviour
System Validation is the field that studies the fundamentals of system communication and information processing. It is the next logical step in computer science and improving software development in general. It allows automated analysis based on behavioural models of a system to see if a system works correctly. We want to guarantee that the systems does exactly what it is supposed to do. The techniques put forward in system validation allow to prove the absence of errors.
-
Course by
-
Self Paced
-
5 hours
-
English
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.
-
Course by
-
Self Paced
-
13 hours
-
English
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 274.99 + VAT
Buy Now
Supervised Text Classification for Marketing Analytics
Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual overview of supervised machine learning and dive into real-world datasets through instructor-led tutorials in Python.
-
Course by
-
Self Paced
-
12 hours
-
English
Supervised Learning and Its Applications in Marketing
Welcome to the Supervised Learning and Its Applications in Marketing course! Supervised learning is the process of making an algorithm to learn to map an input to a particular output. Supervised learning algorithms can help make predictions for new unseen data. In this course, you will use the Python programming language, which is an effective tool for machine learning applications. You will be introduced to the supervised learning techniques: regression and classification. The course will focus on the applications of these techniques in the domain of marketing.
-
Course by
-
Self Paced
-
22 hours
-
English