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Machine Learning with Python: from Linear Models to Deep Learning
Description Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand…
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Applied AI with DeepLearning
Description >>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area
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Advanced Machine Learning Specialization
Description This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able…
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Machine Learning: Classification
Description Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,…). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when…
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Machine Learning Foundations: A Case Study Approach
Description Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course,…
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Overview of Advanced Methods of Reinforcement Learning in Finance
Description In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance. In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling…
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Bayesian Methods for Machine Learning
Description People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine. When applied to deep…
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Practical Reinforcement Learning
Description Welcome to the Reinforcement Learning course. Here you will find out about: – foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. — with math & batteries included – using deep neural networks for RL tasks — also known as “the hype train” – state of the art RL algorithms — and how…
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Deep Neural Networks with PyTorch
Description The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch’s tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions,…
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AI Workflow: Machine Learning, Visual Recognition and NLP
Description This is the fourth 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 4 covers the next stage of the workflow, setting up…