Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
Our Courses
機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. This first course of the two would focus more on mathematical tools, and the other course would focus more on algorithmic tools. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。我們的兩項姊妹課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。本課程將較為著重數學類的工具，而另一課程將較為著重方法類的工具。]
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Course by
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Self Paced
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Chinese
머신 러닝 자세히 알아보기: 기술적 팁, 요령, 그리고 함정
머신 러닝. 여러분 팀이 필요로 하는 것, 여러분 상사가 요구하는 것, 그리고 여러분의 커리어가 사랑하는 것입니다. LinkedIn은 '기업이 가장 필요로 하는 역량' 중 하나이자 미국 내 가장 부상하고 있는 직군으로 머신 러닝을 꼽았습니다. 머신 러닝(일명 '예측 분석')을 배포하기 위해서는, 그 작동 원리를 알아야 합니다. 자신이 실무자가 아닌 기업가라 해도(직접 수치를 다루지 않는 경우), 전반적인 프로젝트를 처리하기 위해서는 머신 러닝의 기본 메커니즘을 파악하고 있어야 합니다. 다시 말해 경영진, 의사결정권자, 또는 운영 관리자 등 직무와 관계없이 예측 모델을 통합하여 의사 결정을 내리는 방법을 감독하는 사람이라면, 가지고 있는 정보가 많을수록 더 나은 결과를 얻을 수 있습니다. 작동 원리를 자세히 살펴보는 일은 아주 재미있을 겁니다. 머신 러닝의 메커니즘은 흥미롭고 놀라울 뿐 아니라, 직관적으로도 이해가 쉽습니다. 전 세계적으로 머신 러닝의 영향력이 빠르게 커지고 있습니다. 이제는 데이터 예측력을 입증하고, 이를 과학적으로 활용하는 방법을 명확히 해야 할 때입니다. 본 강좌는 머신 러닝의 작동 원리를 다루고 있습니다.
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Self Paced
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Korean
مطوّر الواجهة الخلفية من Meta
Ready to gain new skills and the tools developers use to create websites and web applications?
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Self Paced
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Arabic
توقع حضور المواعيد الطبية باستخدام Python
في نهاية المشروع ده هتقدر تصمم model ذكاء صناعي عشان يتوقع المريض هيجي المعاد إلي كان محدد ولا لاباستخدام Python و Jupyter Notebook. خلال المشروع هنمشى مع بعض خطوة بخطوة عشان نقدر نحلل البيانات إلي هتكون معنا من website Kaggle.com الdata دي هتكون عن مرضى في البرازيل.و هنقدر نحدد ازاي الmachine learning engineer بيختار الmachine learning model بتاعو. و ازاي إقدر إستعمل ال-machine learning model بتاعي ده عشان اتوقع هل المريض ده هيجي ولا لا. المشروع ده هيفيد الناس المهتمة بمجال الdata science.
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Self Paced
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2 hours
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Arabic
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.
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Self Paced
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3 hours
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English
Wrangling Data in the Tidyverse
Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This course addresses the problem of wrangling your data so that you can bring them under control and analyze them effectively.
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Self Paced
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14 hours
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English
What is Machine Learning?
This series of short videos and accompanying in-classroom lessons will introduce you and your students to how artificial intelligence works and why it matters. You can choose individual lessons based on topic, explore the whole series of topics in one class or teach all 7 lessons to give a deeper dive. Join our movement and register at https://studio.code.org/users/sign_in and learn more at https://code.org/ai/how-ai-works and at https://studio.code.org/courses
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3 min
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English
What is Data Science?
Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, you will be able to answer this question, understand what data science is and what data scientists do, and learn about career paths in the field. The art of uncovering insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and accurately predicted the Nile River's flooding every year. Since then, people have continued to use data to derive insights and predict outcomes.
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Self Paced
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19 hours
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English
Web of Data
This MOOC – a joint initiative between EIT Digital, Université de Nice Sophia-Antipolis / Université Côte d'Azur, and INRIA - introduces the Linked Data standards and principles that provide the foundation of the Semantic web. You will learn how to publish, obtain and use structured data directly from the Web.
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Self Paced
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18 hours
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English
Web Applications and Command-Line Tools for Data Engineering
In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale.
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Self Paced
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15 hours
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English
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.
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Self Paced
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1 hour
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English
Visual Machine Learning with Yellowbrick
Welcome to this project-based course on Visual Machine Learning with Yellowbrick.
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3 hours
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English
Using SAS Viya REST APIs with Python and R
SAS Viya is an in-memory distributed environment used to analyze big data quickly and efficiently. In this course, you’ll learn how to use the SAS Viya APIs to take control of SAS Cloud Analytic Services from a Jupyter Notebook using R or Python. You’ll learn to upload data into the cloud, analyze data, and create predictive models with SAS Viya using familiar open source functionality via the SWAT package -- the SAS Scripting Wrapper for Analytics Transfer. You’ll learn how to create both machine learning and deep learning models to tackle a variety of data sets and complex problems.
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Self Paced
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18 hours
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English
Using R for Regression and Machine Learning in Investment
In this course, the instructor will discuss various uses of regression in investment problems, and she will extend the discussion to logistic, Lasso, and Ridge regressions. At the same time, the instructor will introduce various concepts of machine learning. You can consider this course as the first step toward using machine learning methodologies in solving investment problems. The course will cover investment analysis topics, but at the same time, make you practice it using R programming.
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Self Paced
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18 hours
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English
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading.
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Course by
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Self Paced
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19 hours
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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.
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Self Paced
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13 hours
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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.
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Self Paced
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3 hours
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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.
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Self Paced
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23 hours
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English
Unsupervised Learning, Recommenders, Reinforcement Learning
In the third course of the Machine Learning Specialization, you will: • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.
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Self Paced
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28 hours
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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.
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Self Paced
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22 hours
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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.
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Self Paced
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38 hours
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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.
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Self Paced
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3 hours
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English
Training AI with Humans
In the course "Training AI with Humans", you'll delve into the intersection of machine learning and human collaboration, exploring how to enhance AI performance through effective data annotation and crowdsourcing.
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Self Paced
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.
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Self Paced
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29 hours
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English
Topic Modeling using PyCaret
In this 1-hour long project-based course, you will create an end-to-end Topic model using PyCaret a low-code Python open-source Machine Learning library.
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Self Paced
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2 hours
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English