Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
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
Deploy a predictive machine learning model using IBM Cloud
In this 1-hour long project-based course, you will be able to create, evaluate and save a machine learning model (without writing a single line of code) using Watson Studio on IBM Cloud Platform, and you will make deployment of the model and try out as a web service frontend to make predictions.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
機器學習基石上 (Machine Learning Foundations)---Mathematical Foundations
Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. This first course of the two would focus more on mathematical tools, and the other course would focus more on algorithmic tools. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。我們的兩項姊妹課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。本課程將較為著重數學類的工具，而另一課程將較為著重方法類的工具。]
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Course by
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Self Paced
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صيني
مطوّر الواجهة الخلفية من Meta
Ready to gain new skills and the tools developers use to create websites and web applications?
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Course by
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Self Paced
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عربي
Application using Amazon Rekognition
في اخر الكورس هتقدر تستخدم AWS Rekognition من الWesbite بتاع AWS . خلال المشروع هتقدر تستخدم AWS Rekognition APIs في Python code وهتقدر تعمل مشاريع Computer Vision, من غير ما تدخل في تفاصيل بناء Machine Learning Model,هتقدر كمان تستخدم AWS High Level Services وتخليها تعمل الوظيفة المطلوبة بسرعة ودقة
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Course by
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التعلم الذاتي
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3 ساعات
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عربي
برنامج تنبيه سطح المكتب باستخدام Python: إشعارات Covid-19
فى نهاية هذا المشروع ، سوف تكون قادرًا على أن تصنع إشعارات مخصصة لل desktop بتاعك باستخدام Python. و هتعرف تستخدم بطريقه مفيده مكتبات Python مختلفه عشان تقدر تطلع بيانات من الانترنت و تقوم بمعالجتها و بعد كده تقدمها ك إشعارات. في المشروع ده هتعمل اخبار COVID-19 ك إشعارات عشان تبقى دايما عارف كل حاجه جديده اول باول.
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Course by
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Self Paced
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3 ساعات
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عربي
توقع حضور المواعيد الطبية باستخدام Python
في نهاية المشروع ده هتقدر تصمم model ذكاء صناعي عشان يتوقع المريض هيجي المعاد إلي كان محدد ولا لاباستخدام Python و Jupyter Notebook. خلال المشروع هنمشى مع بعض خطوة بخطوة عشان نقدر نحلل البيانات إلي هتكون معنا من website Kaggle.com الdata دي هتكون عن مرضى في البرازيل.و هنقدر نحدد ازاي الmachine learning engineer بيختار الmachine learning model بتاعو. و ازاي إقدر إستعمل ال-machine learning model بتاعي ده عشان اتوقع هل المريض ده هيجي ولا لا. المشروع ده هيفيد الناس المهتمة بمجال الdata science.
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Course by
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Self Paced
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2 ساعات
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عربي
تحليل البيانات ب R: التنبؤ بتحليل الانحدار
خلال هل مشروع راح تكون قادر تعمل predictive data analysis with regression يعني إستخدام البيانات للتحليل والتنبؤ من خلال طريقة الانحدار الخطّي ب-R Programming language.
بأستعمال و تعلّم كيف تنفّذ هيك مشروع بR رح تكون عم تستعمل أهم programing language لتحليل البيانات، و عم تبني model تعتبر الحجر الأساس بال machine learning و تستخدم طريقة الـ predictive regression المستعملة بأغلب مجالات الاقتصاد. هيدا المشروع مخصص للprogramers و الdata analysts الي عندها خبرة متواضعة بR و بال Machine Learning و عبالها تتعمّق أكثر و تكتشف كيف بينعمل التحليل التنبؤي بالانحدار.
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Course by
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Self Paced
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2 ساعات
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عربي
Advanced Data Science Capstone
This project completer has proven a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case where he justifies architectural decisions, proves understanding the characteristics of different algorithms, frameworks and technologies and how they impact model performance and scalability.
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Course by
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9 ساعات
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الإنجليزية
K-Means Clustering 101: World Happiness Report
In this case study, we will train an unsupervised machine learning algorithm to cluster countries based on features such as economic production, social support, life expectancy, freedom, absence of corruption, and generosity. The World Happiness Report determines the state of global happiness. The happiness scores and rankings data has been collected by asking individuals to rank their life from 0 (worst possible life) to 10 (best possible life).
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
MongoDB Aggregation Framework
This course will teach you how to perform data analysis using MongoDB's powerful Aggregation Framework. You'll begin this course by building a foundation of essential aggregation knowledge. By understanding these features of the Aggregation Framework you will learn how to ask complex questions of your data.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Encoder-Decoder Architecture
This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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Course by
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Self Paced
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1 ساعات
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الإنجليزية
Object Detection 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. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access.
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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 Digital health
This course introduces the field of digital health and the key concepts and definitions in this emerging field. The key topics include Learning Health Systems and Electronic Health Records and various types of digital health technologies to include mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data.
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Course by
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Self Paced
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31 ساعات
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الإنجليزية
Supervised Text Classification for Marketing Analytics
Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual overview of supervised machine learning and dive into real-world datasets through instructor-led tutorials in Python.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Machine Learning Capstone
This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning Professional Certificate. In this course, you will also learn to build a course recommender system, analyze course-related datasets, calculate cosine similarity, and create a similarity matrix.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Image Classification 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. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Hands-on with AWS for IT Professionals
This course gets hands-on by teaching how to create a new AWS Account, create an Administrative User, and explore the AWS Free Tier. Students can then follow demonstration and explainer videos containing on how AWS Services can combine to create solutions that can be useful in real-life scenarios. The scenarios are grouped into three major categories: Data, Operations, and Architecture. In the data scenario, the instructors will show how a Machine Learning solution automatically redacts PII (Personal Identifiable Information) when data gets retrieved from an Amazon S3 bucket.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Build Decision Trees, SVMs, and Artificial Neural Networks
There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more.
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Course by
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Self Paced
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22 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
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
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
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