Machine Learning courses

3.8

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape...

Preparing for a Machine Learning (ML) interview could be quite challenging. Where to start? What topics to focus on? Theory or practice? Well, fear not! In this course, you will learn to answer 30 non-trivial...

3.08

B​uilding ARIMA models in Python to make demand predictionsD​eveloping the framework for more advanced neural netowrks (such as LSTMs) by understanding autocorrelation and autoregressive models.

Why Learn Foundations of Cloud Computing? This beginner course covers the foundations of cloud computing in a platform-agnostic way. What is a cloud? What are the types of cloud services? Who owns the cloud? Which...

4.75

Learners will be able to explain what Computer Vision is and give examples of Computer Vision tasks. ​Learners will be able to describe the process behind classic algorithmic solutions to Computer Vision tasks and explain...

The rise of machine learning (almost sounds like "rise of the machines"? ) and applications of statistical methods to marketing have changed the field forever. Machine learning is being used to optimize customer journeys which...

The Association of Certified Fraud Examiners estimates that fraud costs organizations worldwide $3. 7 trillion a year and that a typical company loses five percent of annual revenue due to fraud. Fraud attempts are expected...

Among all of the business domains, HR is still the least disrupted. However, the latest developments in data collection and analysis tools and technologies allow for data driven decision-making in all dimensions, including HR. This...

Machine learning is the field that teaches machines and computers to learn from existing data to make predictions on new data: Will a tumor be benign or malignant? Which of your customers will take their...

4.79

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and...

5

This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that...

4.53

In this course you will learn how to quickly and easily get started with Artificial Intelligence using IBM Watson. You will understand how Watson works, become familiar with its use cases and real life client...

4.68

Identify appropriate applications of AI and machine learning within a given business situation. Formulate a machine learning approach to solve specific business problems. Select appropriate tools to solve given machine learning problems. Protect data...

TensorFlow is a state-of-the-art machine learning framework that specializes in the ability to develop deep learning neural networks. And now, it's available in R! This course will walk you through the basics of using TensorFlow...

So what is all this AI fuss about? Machine Learning, Deep Learning, Predictive Analytics - what is the reality behind the hype? How do machines actually learn and what are their limits? How can we...

Machine Learning, Health Data Processing

In this course you'll learn all about using linear classifiers, specifically logistic regression and support vector machines, with scikit-learn. Once you've learned how to apply these methods, you'll dive into the ideas behind them and...

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...

4.66

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc. ) as well as demonstrate how these models can solve co...

4.02

Identify and use core technologies required to support effective MLOps. Configure and provision Google Cloud architectures for reliable and effective MLOps environments. Adopt the best CI/CD practices in the context of ML systems. Implement reliable...