Generative AI for Business Leaders
This condensed course is designed to equip business leaders with an essential understanding of generative artificial intelligence.
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This Pathway will throw light upon how it is imperative for individuals and businesses to employ Cybersecurity in the usage of IoT.
How Google does Machine Learning
This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning.
دوراتنا
Relational Database Support for Data Warehouses
Relational Database Support for Data Warehouses is the third course in the Data Warehousing for Business Intelligence specialization. In this course, you'll use analytical elements of SQL for answering business intelligence questions. You'll learn features of relational database management systems for managing summary data commonly used in business intelligence reporting. Because of the importance and difficulty of managing implementations of data warehouses, we'll also delve into storage architectures, scalable parallel processing, data governance, and big data impacts.
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71 ساعات 
الإنجليزية
AED 239.99 + VAT
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Econometrics: Methods and Applications
Welcome! Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making. * What do I learn? When you know econometrics, you are able to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from macroeconomics to finance and marketing.
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- التعلم الذاتي
- 66 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الاحترافية @ AED 149 + VAT
- الباقة الإبتدائية @ AED 99 + VAT
Math behind Moneyball
Learn how probability, math, and statistics can be used to help baseball, football and basketball teams improve, player and lineup selection as well as in game strategy.
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- التعلم الذاتي
- 65 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Algorithms, Part II
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free.
- مقدم بواسطة
- التعلم الذاتي
- 63 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Geospatial Analysis Project
In this project-based course, you will design and execute a complete GIS-based analysis – from identifying a concept, question or issue you wish to develop, all the way to final data products and maps that you can add to your portfolio. Your completed project will demonstrate your mastery of the content in the GIS Specialization and is broken up into four phases:
Milestone 1: Project Proposal - Conceptualize and design your project in the abstract, and write a short proposal that includes the project description, expected data needs, timeline, and how you expect to complete it.
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- التعلم الذاتي
- 62 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Introduction to Deep Learning
Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs).
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- التعلم الذاتي
- 60 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
R Programming
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
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- التعلم الذاتي
- 57 ساعات
- الإنجليزية
AED 170.99 + VAT
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The Structured Query Language (SQL)
In this course you will learn all about the Structured Query Language ("SQL".) We will review the origins of the language and its conceptual foundations. But primarily, we will focus on learning all the standard SQL commands, their syntax, and how to use these commands to conduct analysis of the data within a relational database.
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- 55 ساعات
- الإنجليزية
AED 344.99 + VAT
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Statistical Inference
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance.
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- 54 ساعات
- الإنجليزية
AED 170.99 + VAT
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Algorithms, Part I
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free.
- مقدم بواسطة
- التعلم الذاتي
- 54 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Regression Models
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated.
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- التعلم الذاتي
- 54 ساعات
- الإنجليزية
AED 170.99 + VAT
اعرف المزيد
Probabilistic Deep Learning with TensorFlow 2
Welcome to this course on Probabilistic Deep Learning with TensorFlow! This course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the probabilistic approach to deep learning. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets.
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- التعلم الذاتي
- 53 ساعات
- الإنجليزية
AED 170.99 + VAT
اعرف المزيد
AI Materials
Learn about the materials that have advanced the performance of artificial intelligence, and the machine learning models that could help accelerate the design and development of novel materials. This course defines artificial intelligence (AI) as a machine to which some or all of the functions of the human brain have been delegated. It highlights the need, and explains in an easy-to-understand way how machine learning from artificial intelligence can dramatically accelerate the development of new materials.
- مقدم بواسطة
- التعلم الذاتي
- 50 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Foundations of Sports Analytics: Data, Representation, and Models in Sports
This course provides an introduction to using Python to analyze team performance in sports. Learners will discover a variety of techniques that can be used to represent sports data and how to extract narratives based on these analytical techniques. The main focus of the introduction will be on the use of regression analysis to analyze team and player performance data, using examples drawn from the National Football League (NFL), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier LEague (EPL, soccer) and the Indian Premier League (IPL, cricket).
- مقدم بواسطة
- التعلم الذاتي
- 49 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Machine Learning Modeling Pipelines in Production
**Starting May 8, enrollment for the Machine Learning Engineering for Production Specialization will be closed.
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- التعلم الذاتي
- 48 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Decision Making and Reinforcement Learning
This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate feedback. We will then model decision problems as finite Markov decision processes (MDPs), and discuss their solutions via dynamic programming algorithms.
- مقدم بواسطة
- التعلم الذاتي
- 47 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Design and Build a Data Warehouse for Business Intelligence Implementation
The capstone course, Design and Build a Data Warehouse for Business Intelligence Implementation, features a real-world case study that integrates your learning across all courses in the specialization.
- مقدم بواسطة
- التعلم الذاتي
- 45 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Statistical Thinking for Industrial Problem Solving, presented by JMP
Statistical Thinking for Industrial Problem Solving is an applied statistics course for scientists and engineers offered by JMP, a division of SAS. By completing this course, students will understand the importance of statistical thinking, and will be able to use data and basic statistical methods to solve many real-world problems.
- مقدم بواسطة
- التعلم الذاتي
- 44 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Regression Analysis
The "Regression Analysis" course equips students with the fundamental concepts of one of the most important supervised learning methods, regression. Participants will explore various regression techniques and learn how to evaluate them effectively. Additionally, students will gain expertise in advanced topics, including polynomial regression, regularization techniques (Ridge, Lasso, and Elastic Net), cross-validation, and ensemble methods (bagging, boosting, and stacking).
- مقدم بواسطة
- التعلم الذاتي
- 40 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
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.
- مقدم بواسطة
- التعلم الذاتي
- 40 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Robotic Path Planning and Task Execution
This course, which is the last and final course in the Introduction to Robotics with Webots specialization, will teach you basic approaches for planning robot trajectories and sequence their task execution. In "Robotic Path Planning and Task Execution", you will develop standard algorithms such as Breadth-First Search, Dijkstra's, A* and Rapidly Exploring Random Trees through guided exercises.
- مقدم بواسطة
- التعلم الذاتي
- 39 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Classification Analysis
The "Classification Analysis" course provides you with a comprehensive understanding of one of the fundamental supervised learning methods, classification. You will explore various classifiers, including KNN, decision tree, support vector machine, naive bayes, and logistic regression, and learn how to evaluate their performance. Through tutorials and engaging case studies, you will gain hands-on experience and practice in applying classification techniques to real-world data analysis tasks. By the end of this course, you will be able to: 1.
- مقدم بواسطة
- التعلم الذاتي
- 38 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Machine Learning: Concepts and Applications
This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques.
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- التعلم الذاتي
- 38 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform.
- مقدم بواسطة
- التعلم الذاتي
- 38 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT
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.
- مقدم بواسطة
- التعلم الذاتي
- 38 ساعات
- الإنجليزية
الاشتراك الشهري
متضمن في- الباقة الإبتدائية @ AED 99 + VAT
- الباقة الاحترافية @ AED 149 + VAT