Artificial Intelligence
In this training, the trainees will learn Artificial Intelligence from the basics to advanced concepts, including Inferential Statistics, A/B Testing, Regression, Clustering, Decision Trees, Random Forests and more. They will work on live projects to understand how to implement the learnings from the program. They will also get graded and non-graded assessments based on real-life challenges and scenarios in the field.
Computer Vision
This Computer Vision training provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, Multiview geometry including stereo, motion estimation and tracking, and classification. We’ll develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment (e.g., panoramas), tracking, and action recognition. The focus of the training is to develop the intuitions and mathematics of the methods in lecture, and then to learn about the difference between theory and practice in the problem sets.
Data Mining
This Data Mining training will introduce you to prominent Data Mining concepts. The training begins by introducing you to data description concepts. You will understand the basics of data, data manipulation, and skewness using histograms in the first half of the training. We will then train to visualize outliers using boxplots, correlation using scatter plots, and understand what machine learning is. You will also understand regression analysis, multiple linear regression, and logistic regression, with demonstrated examples in the latter part of this training.
Deep Learning
This Deep Learning training gives an informative introduction to deep learning and introducing neural networks. The objective of the training is to understand the core principles of deep learning and to be able to execute all factors of the framework of neural nets.
- It would be advisable to complete the Intro to Data and Machine Learning training before starting.
Machine Learning
This Machine Learning training begins with an introduction to AI and ML, before moving onto explain the different levels of users in the field. Then we take a look at out-of-the-box solutions for AI and ML, before looking at a case study to give you the topics covered during this training in a real-world example. By the end of this training, you’ll hopefully understand how to take more advanced trainings and even a springboard into handling complex tasks in your day-to-day job.
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It is recommended that you have a basic familiarity with one of the cloud providers, especially AWS or GCP. Azure, Oracle, and other providers also have machine learning suites but these two are the focus for this class.
Natural Language Processing
This training is aimed at machine learning beginners who want to gain a familiarity with Natural Language Processing (NLP) concepts. After completing this training, trainees will be able to classify text and gather insights. Additionally, trainees will gain a familiarity with the Amazon Comprehend UI, and basic Python concepts for interacting with NLP APIs.
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A basic understanding of Python
Neural Networks
This Neural Networks training gives an informative introduction to deep learning and introducing neural networks. The objective of the training is to understand the core principles of deep learning and to be able to execute all factors of the framework of neural nets.
- It would be advisable to complete the Intro to Data and Machine Learning training before starting.
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