Machine Learning courses

3.5

In this course, we will build on our knowledge of basic models and explore advanced AI techniques. We’ll start with a deep dive into neural networks, building our knowledge from the ground up by examining...

3.97

Design basic quantitative trading strategies Use Keras and Tensorflow to build machine learning modelsBuild a pair trading strategy prediction model and back test it. Build a momentum-based trading model and back test it.

4

I​mplement and evaluate machine learning models (neural networks, random forests, etc. ) on scientific data in Python

4.8

The basics of a machine learning systemHow to deploy a machine learning model to a microcontrollerHow to use machine learning to make decisions and predictions in an embedded system

4.67

Estimate treatment effects using data from randomized control trialsExplore methods to interpret diagnostic and prognostic modelsApply natural language processing to extract information from unstructured medical data

Have you ever wondered how companies like Facebook and Google are able to serve you surprisingly targeted ads that you occasionally click? Well, behind the scenes, they are running sophisticated machine learning models and using...

4.59

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...

4.75

How to train and develop an image classification system using machine learningHow to train and develop an object detection system using machine learningHow to deploy a machine learning model to a microcontroller

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...

4.8

In this course, you will: • Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool...

4.72

Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.

4.35

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

4.18

Like many other areas of machine learning research, reinforcement learning (RL) is evolving at breakneck speed. Just as they have done in other research areas, researchers are leveraging deep learning to achieve state-of-the-art results. In...

4.42

Summarize common sources of ethical risks. Detect and mitigate ethical risks. Evaluate risk identification and mitigation strategies within the lifecycle. Analyze a sample AI model and create a plan to mitigate any identified risks.

4.86

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn...

4.62

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...

3.51

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...

Have you ever wondered how to properly prepare for a Machine Learning Interview? Of course you have or you likely wouldn't be reading this right now! In this course, students will prepare to answer 15...

Dimensionality reduction techniques are based on unsupervised machine learning algorithms and their application offers several advantages. In this course you will learn how to apply dimensionality reduction techniques to exploit these advantages, us...

Time series data is all around us; some examples are the weather, human behavioral patterns as consumers and members of society, and financial data. In this course, you'll learn how to calculate technical indicators from...