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Our Courses
Explaining machine learning models
In this 2-hour long project-based course, you will learn how to understand the predictions of your model, feature relations, visualize and interpret feature & model relation with statistics and much more.
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Course by
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Self Paced
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2 hours
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English
AWS Data Processing
AWS: Data Processing Course is the second course of AWS Certified Data Analytics Specialty Specialization. This course focuses on providing data processing solutions. The entire course is designed to teach learners the concept of EMR and Extract, Transform and Load. This course also put emphasis on ETL services and Data Processing solutions in AWS. The course is divided into three modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 3:30-4:00 Hours Video lectures that provide both Theory and Hands -On knowledge.
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Course by
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Self Paced
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6 hours
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English
Interactive Machine Learning Dashboards using Plotly Dash
In this 2 hour long project-based course, you will learn how to create an HTML outline of a Plotly Dash dashboard.
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Course by
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Self Paced
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3 hours
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English
Genomic Data Science and Clustering (Bioinformatics V)
How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world?
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Course by
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Self Paced
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10 hours
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English
Real Time Machine Learning with Cloud Dataflow and Vertex AI
This is a self-paced lab that takes place in the Google Cloud console. Implement a real-time, streaming machine learning pipeline that uses Cloud Dataflow and Vertex AI.…
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Course by
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Self Paced
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English
Fraud Detection on Financial Transactions with Machine Learning on Google Cloud
This is a self-paced lab that takes place in the Google Cloud console. Explore financial transactions data for fraud analysis, apply feature engineering and machine learning techniques to detect fraudulent activities using BigQuery ML.
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Course by
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Self Paced
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2 hours
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English
Finalize a Data Science Project
This course is designed for business professionals that want to learn how to gather results from previous stages of the data science project and present them to stakeholders. Learners will communicate the results of a model to stakeholders, be shown how to build a basic web app to demonstrate machine learning models and implement and test pipelines that automate the model training, tuning and deployment processes.
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Course by
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Self Paced
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12 hours
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English
Apply Generative Adversarial Networks (GANs)
In this course, you will: - Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa) - Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures - Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to
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Course by
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Self Paced
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27 hours
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English
Cloud Computing Foundations
Welcome to the first course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines. You will also learn how to apply Agile software development techniques to projects which will be useful in building portfolio projects and global-scale Cloud infrastructures. This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering.
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Course by
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Self Paced
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19 hours
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English
Introduction to Machine Learning in Sports Analytics
In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes.
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Course by
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Self Paced
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13 hours
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English
Deploy Machine Learning Models in Azure
Did you know that there is more than one way you can deploy models in Azure? This Guided Project “Deploy machine learning models in Azure” is for everybody working with ml models in Azure . In this 1-hour long project-based course, you will learn how to deploy machine learning models from Portal in Azure, deploy machine learning models in Azure from Python script and deploy machine learning models using Azure CLI. To achieve this, we will use one example data, train a machine learning model, prepare all the files needed for deployment and deploy it!
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Course by
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Self Paced
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3 hours
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English
Diabetes Disease Detection with XG-Boost and Neural Networks
In this project-based course, we will build, train and test a machine learning model to detect diabetes with XG-boost and Artificial Neural Networks. The objective of this project is to predict whether a patient has diabetes or not based on their given features and diagnostic measurements such as number of pregnancies, insulin levels, Body mass index, age and blood pressure.
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Course by
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Self Paced
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2 hours
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English
Graduate Admission Prediction with Pyspark ML
In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project.
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Course by
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Self Paced
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2 hours
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English
Introduction to Machine Learning on AWS
In this course, we start with some services where the training model and raw inference is handled for you by Amazon. We'll cover services which do the heavy lifting of computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training and virtual agents. You'll think of your current solutions and see where you can improve these solutions using AI, ML or Deep Learning. All of these solutions can work with your current applications to make some improvements in your user experience or the business needs of your application.
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Course by
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7 hours
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English
Build a Classification Model using PyCaret
In this 1-hour long project-based course, you will create an end-to-end classification model using PyCaret a low-code Python open-source Machine Learning library.
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Course by
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Self Paced
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3 hours
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English
Topic Modeling using PyCaret
In this 1-hour long project-based course, you will create an end-to-end Topic model using PyCaret a low-code Python open-source Machine Learning library.
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Course by
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Self Paced
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2 hours
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English
Generative Deep Learning with TensorFlow
In this course, you will: a) Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image.
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Course by
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Self Paced
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17 hours
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English
Preparing for AI-900: Microsoft Azure AI 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 AI-900 Microsoft Azure AI Fundamentals certification exam. You will refresh your knowledge of fundamental principles of machine learning on Microsoft Azure. You will go back over the main consideration of AI workloads and the features of computer vision, Natural Language Processing (NLP), and conversational AI workloads on Azure.
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Course by
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Self Paced
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10 hours
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English
AI Workflow: AI in Production
This is the sixth 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. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces&nb
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Course by
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Self Paced
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17 hours
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English
Artificial Intelligence Privacy and Convenience
In this course, we will explore fundamental concepts involved in security and privacy of machine learning projects. Diving into the ethics behind these decisions, we will explore how to protect users from privacy violations while creating useful predictive models. We will also ask big questions about how businesses implement algorithms and how that affects user privacy and transparency now and in the future.
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Course by
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Self Paced
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6 hours
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English
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
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Course by
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Self Paced
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16 hours
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English
Principal Component Analysis with NumPy
Welcome to this 2 hour long project-based course on Principal Component Analysis 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 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 hours
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English
AWS AutoGluon for Machine Learning Classification
Hello everyone and welcome to this new hands-on project on ML classification with AWS AutoGluon. In this project, we will train several machine learning classifiers to detect and classify disease using a super powerful library known as AutoGluon. AutoGluon is the library behind Amazon Web Services (AWS) autopilot and it allows for quick prototyping of several powerful models using a few lines of code.
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Course by
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Self Paced
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2 hours
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English
Reinforcement Learning for Trading Strategies
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data.
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Course by
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Self Paced
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12 hours
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English
Designing the Future of Work
The workplace of tomorrow is an uncertain place. We live in a rapidly changing world, and design innovations such as artificial intelligence (AI), robotics, and big data are rapidly changing the fundamental nature of how we live and work. As these technologies continue to evolve at an exponential rate - it is becoming critical to understand their impact on contemporary work practices, and for businesses and employees to understand how to design a secure future amidst this disruption. What new, disruptive technologies are on the horizon? How will jobs change?
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Course by
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Self Paced
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13 hours
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English