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Analyzing Data with Python

Analyzing Data with Python

In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and perform machine learning using scikit-learn!

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AI for Everyone: Master the Basics

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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  • 33
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Introduction to Machine Learning on AWS

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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Machine Learning at the Edge on Arm: A Practical Introduction

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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Robotic process and intelligent automation for finance

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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Machine learning with Python for finance professionals

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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Dynamic Programming: Applications In Machine Learning and Genomics

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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Hands-on Foundations for Data Science and Machine Learning with Google Cloud Labs

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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  • الإنجليزية
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Fundamentals of Machine Learning for Supply Chain

Fundamentals of Machine Learning for Supply Chain

This course will teach you how to leverage the power of Python to understand complicated supply chain datasets. Even if you are not familiar with supply chain fundamentals, the rich data sets that we will use as a canvas will help orient you with several Pythonic tools and best practices for exploratory data analysis (EDA). As such, though all datasets are geared towards supply chain minded professionals, the lessons are easily generalizable to other use cases.

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  • 13 ساعات
  • الإنجليزية
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IBM Introduction to Machine Learning

IBM Introduction to Machine Learning

Machine learning skills are becoming more and more essential in the modern job market. In 2019, Machine Learning Engineer was ranked as the #1 job in the United States, based on the incredible 344% growth of job openings in the field between 2015 to 2018, and the role’s average base salary of $146,085 (Indeed). This four-course Specialization will help you gain the introductory skills to succeed in an in-demand career in machine learning and data science.

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  • الإنجليزية
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Microsoft Azure Data Scientist Associate (DP-100)

Microsoft Azure Data Scientist Associate (DP-100)

This Professional Certificate is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning s…

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Digital Health

Digital Health

This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services.

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  • الإنجليزية
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DevOps on AWS

DevOps on AWS

DevOps on AWS specialization teaches you how to use the combination of DevOps philosophies, practices and tools to develop, deploy, and maintain applications in the AWS Cloud. Benefits of adopting DevOps include: rapid delivery, reliability, scalability, security and improved collaboration. The first course introduces you to essential AWS products, services, and common solutions.

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  • الإنجليزية
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Machine Learning Rock Star – the End-to-End Practice

Machine Learning Rock Star – the End-to-End Practice

Machine learning reinvents industries and runs the world. Harvard Business Review calls it “the most important general-purpose technology of our era.” But while there are so many how-to courses for hands-on techies, the…

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Introduction to Machine Learning: Supervised Learning

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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  • 40 ساعات
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Deep Learning

Deep Learning

An introduction to the field of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, transformers, generative models, neural network compression and transfer learning. This course will benefit students’ careers as a machine learning engineer or data scientist.

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  • التعلم الذاتي
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Natural Language Processing

Natural Language Processing

Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data.

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Data Science: Machine Learning

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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  • 25
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Practical Data Science on the AWS Cloud

Practical Data Science on the AWS Cloud

Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect…

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Introduction to Image Generation - Français

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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Matrix Methods

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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  • 7 ساعات
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Explainable deep learning models for healthcare - CDSS 3

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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  • 30 ساعات
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Mind and Machine

Mind and Machine

This specialization examines the ways in which our current understanding of human thinking is both illuminated and challenged by the evolving techniques and ideas of artificial intelligence and computer science. Our collective understanding of “minds” – both biological and computational – has been revolutionized over the past half-century by themes originating in fields like cognitive psychology, machine learning, neuroscience, evolutionary psychology, and game theory, among others.

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Mathematics for Machine Learning and Data Science

Mathematics for Machine Learning and Data Science

Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises.

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Business Application of Machine Learning and Artificial Intelligence in Healthcare

Business Application of Machine Learning and Artificial Intelligence in Healthcare

The future of healthcare is becoming dependent on our ability to integrate Machine Learning and Artificial Intelligence into our organizations. But it is not enough to recognize the opportunities of AI; we as leaders in the healthcare industry have to first determine the best use for these applications ensuring that we focus our investment on solving problems that impact the bottom line. Throughout these four modules we will examine the use of decision support, journey mapping, predictive analytics, and embedding Machine Learning and Artificial Intelligence into the healthcare industry.

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  • 13 ساعات
  • الإنجليزية
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