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Our Courses
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 hours
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English
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 hours
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English
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 hours
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English
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.
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Course by
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Self Paced
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3 hours
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English
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 hours
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English
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 hours
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English
Attention Mechanism - Italiano
Questo corso ti introdurrà al meccanismo di attenzione, una potente tecnica che consente alle reti neurali di concentrarsi su parti specifiche di una sequenza di input. Imparerai come funziona l'attenzione e come può essere utilizzata per migliorare le prestazioni di molte attività di machine learning, come la traduzione automatica, il compendio di testi e la risposta alle domande.
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Course by
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Self Paced
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1 hour
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English
Data Analytics in Accounting Capstone
This capstone is the last course in the Data Analytics in Accountancy Specialization. In this capstone course, you are going to take the knowledge and skills you have acquired from the previous courses and apply them to a real-world problem.
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Self Paced
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19 hours
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English
Modeling Data in the Tidyverse
Developing insights about your organization, business, or research project depends on effective modeling and analysis of the data you collect. Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships. This course covers the types of questions you can ask of data and the various modeling approaches that you can apply.
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Course by
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Self Paced
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21 hours
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English
Linear Regression with Python
In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning.
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Course by
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Self Paced
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2 hours
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English
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 hours
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English
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 hours
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English
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 hours
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English
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 hours
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English
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 hours
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English
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 hours
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English
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 hour
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English
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 hours
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English
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 hours
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English
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 hours
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English
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 hours
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Arabic
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 \nالمشروع ده لاي شخص مبتدأ حابب يعمل مشروع او حلول بال Computer Vision باستخدام AWS سواء في دراسته او شغله لتسهيل عملية بناء Machine Learning Model.
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Course by
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Self Paced
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3 hours
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Arabic
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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Arabic
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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Self Paced
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Chinese
Machine Learning con Azure Machine Learning Studio
Este proyecto es un proyecto práctico para aprender a crear modelos de ML con Azure Machine Learning Studio.
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Course by
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Self Paced
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3 hours
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Spanish