Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
AI for Everyone: Master the Basics
Learn what Artificial Intelligence (AI) is by understanding its applications and key concepts including machine learning, deep learning and neural networks.
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Course by
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33
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الإنجليزية
Machine Learning for Semiconductor Quantum Devices
Learn how to deploy artificial intelligence to control and calibrate semiconductor quantum computing chips
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Course by
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Self Paced
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27
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الإنجليزية
Introduction to Machine Learning on AWS
This course is intended for software developers and engineers taking their first steps with the AWS services that do much of heavy lifting of Machine Learning for you.
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Course by
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15
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الإنجليزية
Machine Learning at the Edge on Arm: A Practical Introduction
****This course will provide you with the hands-on experience you’ll need to create innovative machine learning applications using ubiquitous Arm-based microcontrollers.
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Course by
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8
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الإنجليزية
Robotic process and intelligent automation for finance
In this course we explain how automation can play a key role in delivering the requirement to have robust processes and clean data. By using automation tools and machine learning, finance leaders can identify, impl
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Course by
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15
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الإنجليزية
Machine learning with Python for finance professionals
A machine learning course focused on delivering practical Python skills for finance professionals looking to maximise their use of these time-saving tools within their organisation.
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Course by
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الإنجليزية
Dynamic Programming: Applications In Machine Learning and Genomics
Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.
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Course by
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الإنجليزية
Create Machine Learning Models in Microsoft Azure
Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models. This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure.
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Course by
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Self Paced
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13 ساعات
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الإنجليزية
Linear Algebra Basics
Machine learning and data science are the most popular topics of research nowadays. They are applied in all the areas of engineering and sciences. Various machine learning tools provide a data-driven solution to various real-life problems. Basic knowledge of linear algebra is necessary to develop new algorithms for machine learning and data science. In this course, you will learn about the mathematical concepts related to linear algebra, which include vector spaces, subspaces, linear span, basis, and dimension.
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Course by
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21 ساعات
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الإنجليزية
Data Engineering, Big Data, and Machine Learning on GCP
This five-week, accelerated online specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning.
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Course by
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Self Paced
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الإنجليزية
Natural Language Processing in Microsoft Azure
Natural language processing supports applications that can see, hear, speak with, and understand users. Using text analytics, translation, and language understanding services, Microsoft Azure makes it easy to build applications that support natural language. In this course, you will learn how to use the Text Analytics service for advanced natural language processing of raw text for sentiment analysis, key phrase extraction, named entity recognition, and language detection. You will learn how to recognize and synthesize speech by using Azure Cognitive Services.
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Course by
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8 ساعات
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الإنجليزية
MATLAB Programming for Engineers and Scientists
This Specialization is designed for learners with little to no programming experience and teaches them to create MATLAB programs that solve real-world engineering and scientific problems. While the focus is on general computer programming principles, the courses also provide in-depth coverage of MATLAB's unique features for engineering and scientific computing. The first course covers basic programming concepts. The second course teaches techniques for using ChatGPT to program more productively.
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Course by
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Self Paced
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الإنجليزية
Building Cloud Computing Solutions at Scale
With more companies leveraging software that runs on the Cloud, there is a growing need to find and hire individuals with the skills needed to build solutions on a variety of Cloud platforms. Employers agree: Cloud talent is hard to find.
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Course by
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Self Paced
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الإنجليزية
Advanced Data Science with IBM
As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll un…
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Course by
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Self Paced
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الإنجليزية
Explainable deep learning models for healthcare - CDSS 3
This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification.
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Course by
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Self Paced
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30 ساعات
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الإنجليزية
Introduction to Image Generation - Français
Ce cours présente les modèles de diffusion, une famille de modèles de machine learning qui s'est récemment révélée prometteuse dans le domaine de la génération d'images. Les modèles de diffusion trouvent leur origine dans la physique, et plus précisément dans la thermodynamique. Au cours des dernières années, ils ont gagné en popularité dans la recherche et l'industrie. Ils sont à la base de nombreux modèles et outils Google Cloud avancés de génération d'images.
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Course by
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Self Paced
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الإنجليزية
Building a Large-Scale, Automated Forecasting System
In this course you learn to develop and maintain a large-scale forecasting project using SAS Visual Forecasting tools. Emphasis is initially on selecting appropriate methods for data creation and variable transformations, model generation, and model selection.
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Course by
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Self Paced
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10 ساعات
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الإنجليزية
Data Science: Machine Learning
Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.
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Course by
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Self Paced
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25
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الإنجليزية
ESG Investing: Financial Decisions in Flux
As ESG investing continues to evolve towards a global standard, certain initiatives such as the UN’s sustainable development goals, and the Paris Agreement on climate change, have already spurred significant changes across the financial markets. As the title of this specialization suggests, financial decisions by investors, as well as capital deployment by companies, organizations, and governments, have been shifting amid increasing attention to environmental, social, and governance-related concerns. By the end of this specialization, students with basic knowledge of traditional financial pr
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Course by
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Self Paced
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الإنجليزية
Hyperparameter Tuning with Neural Network Intelligence
In this 2-hour long guided project, we will learn the basics of using Microsoft's Neural Network Intelligence (NNI) toolkit and will use it to run a Hyperparameter tuning experiment on a Neural Network. NNI is an open source, AutoML toolkit created by Microsoft which can help machine learning practitioners automate Feature engineering, Hyperparameter tuning, Neural Architecture search and Model compression. In this guided project, we are going to take a look at using NNI to perform hyperparameter tuning.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Hands-on Foundations for Data Science and Machine Learning with Google Cloud Labs
In this Google Cloud Labs Specialization, you'll receive hands-on experience building and practicing skills in BigQuery and Cloud Data Fusion.
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Course by
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Self Paced
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الإنجليزية
Data Analysis and Interpretation
Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights.
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Course by
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Self Paced
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الإنجليزية
Matrix Methods
Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction. Optional examples using Python are used to illustrate the concepts and allow the learner to experiment with the algorithms.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Exploratory Data Analysis in AWS
Exploratory Data Analysis in AWS is the second course in the AWS Certified Machine Learning Specialty specialization. The main focus of this course is to analyze Data Streams and Data Analytics services in AWS along with exploring Data Analysis in AWS. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:00-2:30 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.
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Course by
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5 ساعات
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الإنجليزية
Introduction to Machine Learning: Supervised Learning
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.
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Course by
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Self Paced
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40 ساعات
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الإنجليزية