Coursera

4.76

Formalize problems as Markov Decision Processes Understand basic exploration methods and the exploration / exploitation tradeoff Understand value functions, as a general-purpose tool for optimal decision-making Know how to implement dynamic programm...

4.65

In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver...

4.71

Understand what is AI, its applications and use cases and how it is transforming our livesExplain terms like Machine Learning, Deep Learning and Neural Networks Describe several issues and ethical concerns surrounding AIArticulate advice from...

4.67

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in...

4.5

Use recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition.

4.35

Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.

4.7

Create basic charts and pivot charts in Excel. Explain the important role charts play in telling a data-driven story. Construct advanced charts and visualizations. Build dashboards using Excel and Cognos Analytics.

4.59

Welcome to Data-driven Decision Making. In this course, you'll get an introduction to Data Analytics and its role in business decisions. You'll learn why data is important and how it has evolved. You'll be introduced...

4.5

Describe the importance of data visualization. Relate the history of Matplotlib and its architecture. Apply Matplotlib to create plots using Jupyter notebooks. Discover how to read CSV files into a Pandas DataFrame; process and manipulate...

4.69

We have all heard the phrase “correlation does not equal causation. ” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn...

4.67

Define and explain the key concepts of data clustering Demonstrate understanding of the key constructs and features of the Python language. Implement in Python the principle steps of the K-means algorithm. Design and execute a...

4.71

Where to start with a Data Lake? How to build a secure and scalable Data Lake? What are the common components of a Data Lake? Why do you need a Data Lake and what it's...

4.62

In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and...

4.51

Prepare data, detect statistical data biases, and perform feature engineering at scale to train models with pre-built algorithms.

4.85

In this course, you will: • Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network. • Build...

4.53

Key problems that Machine Learning can address and ultimately help solve. How to build intelligent applications using Amazon AI services like Amazon Comprehend, Amazon Rekognition, Amazon Translate and others. How to build, train and deploy...

4.64

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations s...

4.57

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You...

4.57

This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can...

4.66

In this course, you will: - Assess the challenges of evaluating GANs and compare different generative models - Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs - Identify...