Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Linear Regression with NumPy and Python
Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Fundamentals of Machine Learning in Finance
The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Business Intelligence and Visual Analytics
Building on “Data Warehousing and Business Intelligence,” this course focuses on data visualization and visual analytics. Starting with a thorough coverage of what data visualization is and what type of visualization is good for a given purpose, the course quickly dives into development of practical skills and knowledge about visual analytics by way of using one of the most popular visual analytics tools: SAS Viya, a cloud-based analytics platform. An overview of cloud architecture, automation, and machine learning is also provided.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Perform data science with Azure Databricks
In this course, you will learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run data science workloads in the cloud. This is the fourth course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurec ertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning.
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Course by
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Self Paced
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26 ساعات
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الإنجليزية
Machine Learning Pipelines with Azure ML Studio
In this project-based course, you are going to build an end-to-end machine learning pipeline in Azure ML Studio, all without writing a single line of code! This course uses the Adult Income Census data set to train a model to predict an individual's income. It predicts whether an individual's annual income is greater than or less than $50,000. The estimator used in this project is a Two-Class Boosted Decision Tree classifier. Some of the features used to train the model are age, education, occupation, etc.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Machine Learning for Data Analysis
Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
Life Expectancy Prediction Using Machine Learning
In this hands-on project, we will train a Linear Regression model to predict life expectancy. The dataset was initially obtained from the World Health Organization (WHO) and United Nations Websites. Data contains features such as year, status, life expectancy, adult mortality, infant deaths, percentage of expenditure, and alcohol consumption.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Innovations in Investment Technology: Artificial Intelligence
Explore the evolution of AI investing and online wealth management. Investing and managing your wealth online has never been easier, but how does AI investing work and what are the challenges? On this course, you’ll explore how technology has changed the way we invest money.
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Course by
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Self Paced
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10 ساعات
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الإنجليزية
Machine Learning Introduction for Everyone
This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more. Our labs give you hands-on experience with these machine learning and data science concepts.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Learning TensorFlow: the Hello World of Machine Learning
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn the basic ‘Hello World' of machine learning. Instead of programming explicit rules in a language such as Java or C++, you build a system that is trained on data to infer the rules that determine a relationship between numbers.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
Leveraging Unstructured Data with Cloud Dataproc on Google Cloud em Português Brasileiro
Este curso intensivo de uma semana baseia-se nos cursos anteriores da especialização Data Engineering on Google Cloud Platform.
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Course by
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Self Paced
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الإنجليزية
Scalable Machine Learning on Big Data using Apache Spark
This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Introduction to Generative AI - Português Brasileiro
Este é um curso de microaprendizagem introdutório que busca explicar a IA generativa: o que é, como é usada e por que ela é diferente de métodos tradicionais de machine learning. O curso também aborda as ferramentas do Google que ajudam você a desenvolver apps de IA generativa.
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Course by
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Self Paced
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الإنجليزية
AI Workflow: Data Analysis and Hypothesis Testing
This is the second course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. In this course you will begin your work for a hypothetical streaming media company by doing exploratory data analysis (EDA). Best practices for data visualization, handling missing data, and hypothesis testing will be introduced to you as part of your work. You will learn techniques of estimation
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
ML Algorithms
ML Algorithms is the fourth Course in the AWS Certified Machine Learning Specialty specialization. This Course enables learners to deep dive Machine Learning Algorithms. 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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Self Paced
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5 ساعات
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الإنجليزية
Medical Diagnosis using Support Vector Machines
In this one hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset we are going to use comes from the National Institute of Diabetes and Digestive and Kidney Diseases, and contains anonymized diagnostic measurements for a set of female patients. We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. By the end of this course, you will be able to model an existing dataset with the goal of making predictions about new data.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Demand Forecasting Using Time Series
This course is the second in a specialization for Machine Learning for Supply Chain Fundamentals. In this course, we explore all aspects of time series, especially for demand prediction. We'll start by gaining a foothold in the basic concepts surrounding time series, including stationarity, trend (drift), cyclicality, and seasonality. Then, we'll spend some time analyzing correlation methods in relation to time series (autocorrelation). In the 2nd half of the course, we'll focus on methods for demand prediction using time series, such as autoregressive models.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Machine Learning Models in Science
This course is aimed at anyone interested in applying machine learning techniques to scientific problems. In this course, we'll learn about the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms. We'll start with data preprocessing techniques, such as PCA and LDA. Then, we'll dive into the fundamental AI algorithms: SVMs and K-means clustering. Along the way, we'll build our mathematical and programming toolbox to prepare ourselves to work with more complicated models.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Using SAS Viya REST APIs with Python and R
SAS Viya is an in-memory distributed environment used to analyze big data quickly and efficiently. In this course, you’ll learn how to use the SAS Viya APIs to take control of SAS Cloud Analytic Services from a Jupyter Notebook using R or Python. You’ll learn to upload data into the cloud, analyze data, and create predictive models with SAS Viya using familiar open source functionality via the SWAT package -- the SAS Scripting Wrapper for Analytics Transfer. You’ll learn how to create both machine learning and deep learning models to tackle a variety of data sets and complex problems.
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Computer Vision in Microsoft Azure
In Microsoft Azure, the Computer Vision cognitive service uses pre-trained models to analyze images, enabling software developers to easily build applications"see" the world and make sense of it. This ability to process images is the key to creating software that can emulate human visual perception. In this course, you'll explore some of these capabilities as you learn how to use the Computer Vision service to analyze images. This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Optimizing Machine Learning Performance
This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Introduction to the Tidyverse
This course introduces a powerful set of data science tools known as the Tidyverse. The Tidyverse has revolutionized the way in which data scientists do almost every aspect of their job. We will cover the simple idea of "tidy data" and how this idea serves to organize data for analysis and modeling. We will also cover how non-tidy can be transformed to tidy data, the data science project life cycle, and the ecosystem of Tidyverse R packages that can be used to execute a data science project.
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Course by
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Self Paced
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الإنجليزية
Logistic Regression with NumPy and Python
Welcome to this project-based course on Logistic with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Google Advanced Data Analytics Capstone
You’re almost there! This is the seventh and final course of the Google Advanced Data Analytics Certificate. In this course, you have the opportunity to complete an optional capstone project that includes key concepts from each of the six preceding courses.
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Course by
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Self Paced
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10 ساعات
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الإنجليزية