Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Machine Learning Algorithms
In this course you will: a) understand the naïve Bayesian algorithm. b) understand the Support Vector Machine algorithm. c) understand the Decision Tree algorithm. d) understand the Clustering. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.
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Course by
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Self Paced
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16 ساعات
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الإنجليزية
Create Your First Chatbot with Rasa and Python
In this 2 hour long project-based course, you will learn to create chatbots with Rasa and Python. Rasa is a framework for developing AI powered, industrial grade chatbots. It’s incredibly powerful, and is used by developers worldwide to create chatbots and contextual assistants. In this project, we are going to understand some of the most important basic aspects of the Rasa framework and chatbot development. Once you’re done with this project, you will be able to create simple AI powered chatbots on your own.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Foundations of Data Science
This is the first of seven courses in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace.
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
Statistics for Machine Learning for Investment Professionals
One of the biggest changes in the past decade is the rapid adoption of machine learning, AI, and big data in investment decision making. This course introduces learners with knowledge of the investment industry to foundational statistical concepts underpinning machine learning as well as advanced AI techniques. This course demonstrates core modeling frameworks along with carefully selected real-world investment practice examples. The course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary).
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Naive Bayes 101: Resume Selection with Machine Learning
In this project, we will build a Naïve Bayes Classifier to predict whether a given resume text is flagged or not. Our training data consist of 125 resumes with 33 flagged resumes and 92 non flagged resumes. This project could be practically used to screen resumes in companies.
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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 Machine Learning with AWS and NVIDIA
Machine learning (ML) projects can be complex, tedious, and time consuming. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project.
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more.
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Course by
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Self Paced
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24 ساعات
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الإنجليزية
Predict Baby Weight with TensorFlow on AI Platform
In this lab you train, evaluate, and deploy a machine learning model to predict a baby’s weight. You then send requests to the model to make online predictions. This lab is part of a series of labs on processing scientific data.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
TensorFlow Serving with Docker for Model Deployment
This is a hands-on, guided project on deploying deep learning models using TensorFlow Serving with Docker. In this 1.5 hour long project, you will train and export TensorFlow models for text classification, learn how to deploy models with TF Serving and Docker in 90 seconds, and build simple gRPC and REST-based clients in Python for model inference.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Regression with Automatic Differentiation in TensorFlow
In this 1.5 hour long project-based course, you will learn about constants and variables in TensorFlow, you will learn how to use automatic differentiation, and you will apply automatic differentiation to solve a linear regression problem. By the end of this project, you will have a good understanding of how machine learning algorithms can be implemented in TensorFlow.
In order to be successful in this project, you should be familiar with Python, Gradient Descent, Linear Regression.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Data Science for Business Innovation
This is your chance to learn all about Data Science for Business innovation and future-proof your career. Match your business experience tech and analytics! The Data Science for Business Innovation nano-course is a compendium of the must-have expertise in data science for executives and managers to foster data-driven innovation. The course explains what Data Science is and why it is so hyped.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
CUDA Advanced Libraries
This course will complete the GPU specialization, focusing on the leading libraries distributed as part of the CUDA Toolkit. Students will learn how to use CuFFT, and linear algebra libraries to perform complex mathematical computations. The Thrust library’s capabilities in representing common data structures and associated algorithms will be introduced. Using cuDNN and cuTensor they will be able to develop machine learning applications that help with object detection, human language translation and image classification.
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Course by
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Self Paced
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25 ساعات
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الإنجليزية
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.
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Course by
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Self Paced
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38 ساعات
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الإنجليزية
Interpretable machine learning applications: Part 5
You will be able to use the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model. As a use case, we will be working with the dataset about recidivism, i.e., the likelihood for a former imprisoned person to commit another offence within the first two years, since release from prison. The guided project will be making use of the COMPAS dataset, which already includes predicted as well as actual outcomes.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Code Free Data Science
The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Calculus 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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Course by
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Self Paced
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26 ساعات
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الإنجليزية
Build Image Quality Inspection using AWS Lookout for Vision
In this guided project, you will learn how to build automated image quality inspection using Amazon Lookout for Vision. Amazon Lookout for Vision is a Machine Learning as a Service from Amazon Web services which you could leverage to do Image Analytics and address interesting use cases such as drone detection, defect detection, object detection, smile detection, fall detection without writing a single line of code. Please note: As part of this course, you would need your AWS Account to complete the course. It would be charged as per your usage of AWS Lookout for Vision service.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Project Planning and Machine Learning
This course can also be taken for academic credit as ECEA 5386, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is part 2 of the specialization.
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Cervical Cancer Risk Prediction Using Machine Learning
In this hands-on project, we will build and train an XG-Boost classifier to predict whether a person has a risk of having cervical cancer. Cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. Data has been obtained from 858 patients and include features such as number of pregnancies, smoking habits, Sexually Transmitted Disease (STD), demographics, and historic medical records.
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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: Feature Engineering and Bias Detection
This is the third 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. Course 3 introduces you to the next stage of the workflow for our hypothetical media company. In this stage of work you will learn best practices for feature engineering, handling class imbalances and detecting bias in the data. Class imbalances can seriously affect the validity of your
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Analyze Datasets and Train ML Models using AutoML
In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, you will analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multi-class text classifier.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Advanced Computer Vision with TensorFlow
In this course, you will: a) Explore image classification, image segmentation, object localization, and object detection.
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Course by
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19 ساعات
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الإنجليزية
Interpretable machine learning applications: Part 3
In this 50 minutes long project-based course, you will learn how to apply a specific explanation technique and algorithm for predictions (classifications) being made by inherently complex machine learning models such as artificial neural networks. The explanation technique and algorithm is based on the retrieval of similar cases with those individuals for which we wish to provide explanations.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
ML Pipelines on Google Cloud
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
Interpretable Machine Learning Applications: Part 2
By the end of this project, you will be able to develop intepretable machine learning applications explaining individual predictions rather than explaining the behavior of the prediction model as a whole. This will be done via the well known Local Interpretable Model-agnostic Explanations (LIME) as a machine learning interpretation and explanation model. In particular, in this project, you will learn how to go beyond the development and use of machine learning (ML) models, such as regression classifiers, in that we add on explainability and interpretation aspects for individual predictions.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية