Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Introduction to Generative AI - Italiano
Questo è un corso di microlearning di livello introduttivo volto a spiegare cos'è l'AI generativa, come viene utilizzata e in che modo differisce dai tradizionali metodi di machine learning. Descrive inoltre gli strumenti Google che possono aiutarti a sviluppare le tue app Gen AI.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
Tesla Stock Price Prediction using Facebook Prophet
In this 1.5-hour long project-based course, you will learn how to build a Facebook Prophet Machine learning model in order to forecast the price of Tesla 30 days into the future. We will also visualize the historical performance of Tesla through graphs and charts using Plotly express and evaluate the performance of the model against real data using Google Finance in Google Sheets. We will also dive into a brief stock analysis of Tesla and we will discuss PE ratio, EPS, Beta, Market cap, Volume and price range of Tesla.
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Course by
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Self Paced
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4 ساعات
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الإنجليزية
Visualizing Filters of a CNN using TensorFlow
In this short, 1 hour long guided project, we will use a Convolutional Neural Network - the popular VGG16 model, and we will visualize various filters from different layers of the CNN. We will do this by using gradient ascent to visualize images that maximally activate specific filters from different layers of the model. We will be using TensorFlow as our machine learning framework.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
SVM Regression, prediction and losses
In this 1-hour long project-based course, you will learn how to
Train SVM regression model- with large & small margin, second degree polynomial kernel, make prediction using Linear SVM classifier; how a small weight vector results in a large margin? and finally
pictorial representation for Hinge loss. This project gives you easy access to the invaluable learning techniques used by experts in machine learning.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Data for Machine Learning
This course is all about data and how it is critical to the success of 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
Semantic Segmentation with Amazon Sagemaker
Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project.
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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: an overview
The course provides a general overview of the main methods in the machine learning field. Starting from a taxonomy of the different problems that can be solved through machine learning techniques, the course briefly presents some algorithmic solutions, highlighting when they can be successful, but also their limitations. These concepts will be explained through examples and case studies.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Explainable Machine Learning with LIME and H2O in R
Welcome to this hands-on, guided introduction to Explainable Machine Learning with LIME and H2O in R. By the end of this project, you will be able to use the LIME and H2O packages in R for automatic and interpretable machine learning, build classification models quickly with H2O AutoML and explain and interpret model predictions using LIME.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Simple Nearest Neighbors Regression and Classification
In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making in Python.
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Course by
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Self Paced
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4 ساعات
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الإنجليزية
Introduction to Generative AI
This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
Preparing for DP-900: Microsoft Azure Data Fundamentals Exam
Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses. In this course, you will prepare to take the DP-900 Microsoft Azure Data Fundamentals certification exam. You will refresh your knowledge of the fundamentals of database concepts in a cloud environment, the basic skilling in cloud data services, and foundational knowledge of cloud data services within Microsoft Azure.
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Course by
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Self Paced
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6 ساعات
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الإنجليزية
Supervised Machine Learning: Classification
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models.
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Course by
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Self Paced
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25 ساعات
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الإنجليزية
Introduction to Customer Segmentation in Python
In this 2 hour long project, you will learn how to approach a customer purchase dataset, and how to explore the intricacies of such a dataset. You will learn the basic underlying ideas behind Principal Component Analysis, Kernel Principal Component Analysis, and K-Means Clustering. You will learn how to leverage these concepts, paired with industry knowledge and auxiliary modeling concepts to segment the customers of a certain store, and find similarities and differences between different clusters using unsupervised machine learning techniques.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
AI Workflow: Business Priorities and Data Ingestion
This is the first course of a six part 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. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and ma
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Web Applications and Command-Line Tools for Data Engineering
In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale.
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Course by
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Self Paced
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15 ساعات
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الإنجليزية
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.
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Course by
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Self Paced
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50 ساعات
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الإنجليزية
Artificial Intelligence Algorithms Models and Limitations
We live in an age increasingly dominated by algorithms. As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations in the real world. Whether it's making loan decisions or re-routing traffic, machine learning models need to accurately reflect our shared values. In this course, we will explore the rise of algorithms, from the most basic to the fully-autonomous, and discuss how to make them more ethically sound.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Attention Mechanism
This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
Microsoft Azure Machine Learning for Data Scientists
Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
Clustering analysis and techniques
In this 2-hour long project-based course, you will learn how to perform clustering (one of the core pillar of unsupervised learning) and its importance in machine learning, set up PyCaret Clustering module, create, visualize & compare Clustering algorithms all this with just a few lines of code.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Deploy Machine Learning Model into AWS Cloud Servers
By the end of this project, you will learn how to build a spam detector using machine learning & launch it as a serverless API using AWS Elastic Beanstalk technology. You will be using the Flask python framework to create the API, basic machine learning methods to build the spam detector & AWS desktop management console to deploy the spam detector into the AWS cloud servers. Additionally, you will learn more about how to switch between different versions of your web application & also, monitoring your AWS servers using Elastic Beanstalk Desktop Management Console.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Introduction to Medical Software
In this class, we present a broad overview of the field of medical software. You will learn from Yale professors and a series of industry experts who connect the course concepts to their real world applications. We begin by discussing medical device regulatory structures, data privacy and cybersecurity regulations, and key support technologies such quality management systems and risk management.
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Course by
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Self Paced
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38 ساعات
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الإنجليزية
Advanced Learning Algorithms
In the second course of the Machine Learning Specialization, you will: • Build and train a neural network with TensorFlow to perform multi-class classification • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world • Build and use decision trees and tree ensemble methods, including random forests and boosted trees The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.
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Course by
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Self Paced
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الإنجليزية
Build a Machine Learning Web App with Streamlit and Python
Welcome to this hands-on project on building your first machine learning web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive ML web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code!
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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 Modeling Pipelines in Production
**Starting May 8, enrollment for the Machine Learning Engineering for Production Specialization will be closed.
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Course by
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Self Paced
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48 ساعات
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الإنجليزية