Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
Our Courses
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor.
-
Course by
-
Self Paced
-
43 hours
-
English
Introduction to Data Science and scikit-learn in Python
This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis. We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn.
-
Course by
-
Self Paced
-
14 hours
-
English
Foundations of Data Science
This is the first of seven courses in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace.
-
Course by
-
Self Paced
-
23 hours
-
English
Statistics for Machine Learning for Investment Professionals
One of the biggest changes in the past decade is the rapid adoption of machine learning, AI, and big data in investment decision making. This course introduces learners with knowledge of the investment industry to foundational statistical concepts underpinning machine learning as well as advanced AI techniques. This course demonstrates core modeling frameworks along with carefully selected real-world investment practice examples. The course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary).
-
Course by
-
Self Paced
-
18 hours
-
English
Naive Bayes 101: Resume Selection with Machine Learning
In this project, we will build a Naïve Bayes Classifier to predict whether a given resume text is flagged or not. Our training data consist of 125 resumes with 33 flagged resumes and 92 non flagged resumes. This project could be practically used to screen resumes in companies.
-
Course by
-
Self Paced
-
3 hours
-
English
Hands-on Machine Learning with AWS and NVIDIA
Machine learning (ML) projects can be complex, tedious, and time consuming. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project.
-
Course by
-
Self Paced
-
23 hours
-
English
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more.
-
Course by
-
Self Paced
-
24 hours
-
English
Predict Baby Weight with TensorFlow on AI Platform
In this lab you train, evaluate, and deploy a machine learning model to predict a baby’s weight. You then send requests to the model to make online predictions. This lab is part of a series of labs on processing scientific data.
-
Course by
-
Self Paced
-
2 hours
-
English
Data Science for Business Innovation
This is your chance to learn all about Data Science for Business innovation and future-proof your career. Match your business experience tech and analytics! The Data Science for Business Innovation nano-course is a compendium of the must-have expertise in data science for executives and managers to foster data-driven innovation. The course explains what Data Science is and why it is so hyped.
-
Course by
-
Self Paced
-
7 hours
-
English
CUDA Advanced Libraries
This course will complete the GPU specialization, focusing on the leading libraries distributed as part of the CUDA Toolkit. Students will learn how to use CuFFT, and linear algebra libraries to perform complex mathematical computations. The Thrust library’s capabilities in representing common data structures and associated algorithms will be introduced. Using cuDNN and cuTensor they will be able to develop machine learning applications that help with object detection, human language translation and image classification.
-
Course by
-
Self Paced
-
25 hours
-
English
K-Means Clustering 101: World Happiness Report
In this case study, we will train an unsupervised machine learning algorithm to cluster countries based on features such as economic production, social support, life expectancy, freedom, absence of corruption, and generosity. The World Happiness Report determines the state of global happiness. The happiness scores and rankings data has been collected by asking individuals to rank their life from 0 (worst possible life) to 10 (best possible life).
-
Course by
-
Self Paced
-
3 hours
-
English
Deploy a predictive machine learning model using IBM Cloud
In this 1-hour long project-based course, you will be able to create, evaluate and save a machine learning model (without writing a single line of code) using Watson Studio on IBM Cloud Platform, and you will make deployment of the model and try out as a web service frontend to make predictions.
-
Course by
-
Self Paced
-
2 hours
-
English
Build Decision Trees, SVMs, and Artificial Neural Networks
There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more.
-
Course by
-
Self Paced
-
22 hours
-
English
Hands-on with AWS for IT Professionals
This course gets hands-on by teaching how to create a new AWS Account, create an Administrative User, and explore the AWS Free Tier. Students can then follow demonstration and explainer videos containing on how AWS Services can combine to create solutions that can be useful in real-life scenarios. The scenarios are grouped into three major categories: Data, Operations, and Architecture. In the data scenario, the instructors will show how a Machine Learning solution automatically redacts PII (Personal Identifiable Information) when data gets retrieved from an Amazon S3 bucket.
-
Course by
-
Self Paced
-
2 hours
-
English
Machine Learning Capstone
This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning Professional Certificate. In this course, you will also learn to build a course recommender system, analyze course-related datasets, calculate cosine similarity, and create a similarity matrix.
-
Course by
-
Self Paced
-
19 hours
-
English
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
Introduction to Digital health
This course introduces the field of digital health and the key concepts and definitions in this emerging field. The key topics include Learning Health Systems and Electronic Health Records and various types of digital health technologies to include mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data.
-
Course by
-
Self Paced
-
31 hours
-
English
Encoder-Decoder Architecture
This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
-
Course by
-
Self Paced
-
1 hour
-
English
MongoDB Aggregation Framework
This course will teach you how to perform data analysis using MongoDB's powerful Aggregation Framework. You'll begin this course by building a foundation of essential aggregation knowledge. By understanding these features of the Aggregation Framework you will learn how to ask complex questions of your data.
-
Course by
-
Self Paced
-
19 hours
-
English
مطوّر الواجهة الخلفية من Meta
Ready to gain new skills and the tools developers use to create websites and web applications?
-
Course by
-
Self Paced
-
Arabic
Serverless Data Analysis with Google BigQuery and Cloud Dataflow em Português Brasileiro
Este curso rápido sob demanda tem uma semana de duração e é baseado no Google Cloud Platform Big Data and Machine Learning Fundamentals.
-
Course by
-
Self Paced
-
Portuguese
Machine Learning con Azure Machine Learning Studio
Este proyecto es un proyecto práctico para aprender a crear modelos de ML con Azure Machine Learning Studio.
-
Course by
-
Self Paced
-
3 hours
-
Spanish
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。]
-
Course by
-
Self Paced
-
Chinese
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.
-
Course by
-
Self Paced
-
2 hours
-
Arabic
Advanced Data Science Capstone
This project completer has proven a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case where he justifies architectural decisions, proves understanding the characteristics of different algorithms, frameworks and technologies and how they impact model performance and scalability.
-
Course by
-
9 hours
-
English