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Our Courses
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language.
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English
Investment Management with Python and Machine Learning
The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation.
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AI Product Management
Organizations in every industry are accelerating their use of artificial intelligence and machine learning to create innovative new products and systems. This requires professionals across a range of functions, not just strictly within the data science and data engineering teams, to understand when and how AI can be applied, to speak the language of data and analytics, and to be capable of working in cross-functional teams on machine learning projects. This Specialization provides a foundational understanding of how machine learning works and when and how it can be applied to solve problems.
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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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13 hours
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English
TensorFlow 2 for Deep Learning
This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow. The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning mode
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Machine Learning: Theory and Hands-on Practice with Python
In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems.
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MLOps | Machine Learning Operations
This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow.
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NoSQL, Big Data, and Spark Foundations
Big Data Engineers and professionals with NoSQL skills are highly sought after in the data management industry. This Specialization is designed for those seeking to develop fundamental skills for working with Big Data, Apache Spark, and NoSQL databases.
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DeepLearning.AI TensorFlow Developer
TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects.
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Sports Performance Analytics
Sports analytics has emerged as a field of research with increasing popularity propelled, in part, by the real-world success illustrated by the best-selling book and motion picture, Moneyball.
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TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types.
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IBM Data Science
Prepare for a career in the high-growth field of data science. In this program, you’ll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist in as little as 4 months. No prior knowledge of computer science or programming languages is required. Data science involves gathering, cleaning, organizing, and analyzing data with the goal of extracting helpful insights and predicting expected outcomes.
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Machine Learning on Google Cloud
What is machine learning, and what kinds of problems can it solve? How can you build, train, and deploy machine learning models at scale without writing a single line of code?
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Machine Learning for Kyphosis Disease Classification
The objective of this project is to predict whether a patient has kyphosis or not, based on given features and diagnostic measurements such as age and number of vertebrae. Kyphosis is an abnormally excessive convex curvature of the spine. This guided project is practical and directly applicable to the healthcare industry. You can add this project to your portfolio of projects which is essential for your next job interview.
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3 hours
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English
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Exam Prep MLS-C01: AWS Certified Specialty Machine Learning
Gain Skills to understand the fundamentals of Machine Learning. Learn working with various AWS Services necessary for Machine Learning. Hands on Experience working with AWS Management Console. Prepare for AWS Certified Machine Learning Specialty Certification
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Data Science: Statistics and Machine Learning
Build models, make inferences, and deliver interactive data products. This specialization continues and develops on the material from the Data Science: Foundations using R specialization. It covers statistical inference, regression models, machine learning, and the development of data products. In the Capstone Project, you’ll apply the skills learned by building a data product using real-world data.
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Google Advanced Data Analytics
Get professional training designed by Google and take the next step in your career with advanced data analytics skills. There are over 144,000 open jobs in advanced data analytics and the median salary for entry-level roles is $118,000.¹ Advanced data professionals are responsible for collecting, analyzing, and interpreting extremely large amounts of data.
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Ethics in the Age of AI
As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations. In this specialization, we will explore the rise of algorithms, fundamental issues of fairness and bias in machine learning, and basic concepts involved in security and privacy of machine learning projects. We'll finish with a study of 3 projects that will allow you to put your new skills into action.
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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
Machine/Deep Learning for Mining Quality Prediction-Enhanced
In this hands-on project, we will train machine learning and deep learning models to predict the % of Silica Concentrate in the Iron ore concentrate per minute. This project could be practically used in Mining Industry to get the % Silica Concentrate at much faster rate compared to the traditional methods.
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3 hours
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English
Evaluate Machine Learning Models with Yellowbrick
Welcome to this project-based course on Evaluating Machine Learning Models with Yellowbrick.
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3 hours
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English
Attention Mechanism - Português Brasileiro
Este curso é uma introdução ao mecanismo de atenção, uma técnica avançada que permite que as redes neurais se concentrem em partes específicas de uma sequência de entrada. Você vai entender como a atenção funciona e como ela pode ser usada para melhorar o desempenho de várias tarefas de machine learning (como tradução automática, resumo de texto e resposta a perguntas).
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Advanced Machine Learning on Google Cloud
This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems.
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How to Use Microsoft Azure ML Studio for Kaggle Competitions
In this 90 minutes long project-based course, you will learn how to create a Microsoft Azure ML Studio account, a Kaggle account for competitions and use both of them to build a machine learning model which we will be using to make predictions.
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3 hours
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English