Machine Learning

After completing this course, learners will be able to: represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.; apply common vector and matrix algebra operations like dot product, inverse, and determinants; express certain types of matrix operations as linear [...]

After completing this course, learners will be able to: analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients; approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods; visually interpret [...]

Python Essentials for MLOps is a course designed to provide learners with the fundamental Python skills needed to succeed in an MLOps role. This course covers the basics of the Python programming language, including data types, functions, modules and testing techniques. It also covers how to work effectively with [...]

In the second course of the Computer Vision for Engineering and Science specialization, you will perform two of the most common computer vision tasks: classifying images and detecting objects. You will apply the entire machine learning workflow, from preparing your data to evaluating your results. By the end of [...]

Data science and artificial intelligence are exciting, growing fields with a lot to offer prospective job seekers. However, even with the massive growth in technology and positions, there are still many barriers to entry. This course explores today’s challenges and opportunities within data science and artificial intelligence, the varying [...]

In the third course of the Machine Learning Specialization, you will: Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection; Build recommender systems with a collaborative filtering approach and a content-based deep learning method; Build a deep reinforcement learning [...]

In the first course of the Machine Learning Specialization, you will: build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn; build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic [...]

In the second course of the Machine Learning Specialization, you will: build and train a neural network with TensorFlow to perform multi-class classification; apply best practices for machine learning development so that your models generalize to data and tasks in the real world; build and use decision trees and [...]

The course provides a general overview of the main methods in the machine learning field. Starting from a taxonomy of the different problems that can be solved through machine learning techniques, the course briefly presents some algorithmic solutions, highlighting when they can be successful, but also their limitations. These [...]

Just as teachers help students gain new skills, the same is true of artificial intelligence (AI). Machine learning algorithms can adapt and change, much like the learning process itself. Using the machine teaching paradigm, a subject matter expert (SME) can teach AI to improve and optimize a variety of [...]