Learn Python, NumPy, Pandas, Matplotlib, PyTorch, and Linear Algebra—the foundations for building your own neural network. 

This Nanodegree trains the learner about foundational topics in the exciting field of deep learning, the technology behind state-of-the-art artificial intelligence.

The goal of the AWS Machine Learning Engineer (MLE) Nanodegree program is to equip software developers/data scientists with the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. This program will focus on the latest best practices and capabilities that are enabled by Amazon SageMaker, including new model design/deployment features and case studies in which they can be applied to. 


The Intro to Machine Learning with Pytorch program covers machine learning concepts and techniques, with a focus on supervised and unsupervised learning. The program includes three courses and covers topics such as linear regression, logistic regression, decision trees, Naive Bayes, support vector machines, neural networks, and clustering. The courses include projects that allow learners to apply these techniques to real-world problems, such as identifying potential donors for a charity and clustering customers based on their spending habits. The program uses Python and PyTorch for implementation and includes lessons on model evaluation and tuning.

The Introduction to Machine Learning with TensorFlow program covers supervised and unsupervised learning methods for machine learning. Course 1 introduces regression, perceptron algorithms, decision trees, naive Bayes, support vector machines, and evaluation metrics. Course 2 focuses on neural networks and creating an image classifier. Course 3 covers unsupervised learning methods, including clustering and dimensionality reduction for customer segmentation. Key skills include data preprocessing, model selection, and evaluation using TensorFlow and Python.

Complete real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio.

Master the computer vision skills behind advances in robotics and automation. Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models.

Master the skills to get computers to understand, process, and manipulate human language. Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more.

The Deep Reinforcement Learning Nanodegree has four courses: Introduction to Deep Reinforcement Learning, Value-Based Methods, Policy-Based Methods, and Multi-Agent RL. Students learn to implement classical solution methods, define Markov decision processes, policies, and value functions, and derive Bellman equations. They learn dynamic programming, Monte Carlo methods, temporal-difference methods, deep RL, and apply these techniques to solve real-world problems. They learn to train agents to navigate virtual worlds, generate optimal financial trading strategies, and apply RL to multiple interacting agents.

Become an expert in the core concepts of artificial intelligence and learn how to apply them to real-life problems.

Autonomous Systems

Business

Career Resources 

Cloud Computing

Cybersecurity

Data Science

Executive Leadership

Product Management

Programming & Development

Manufacturing

Telecommunications

Energy

Healthcare

Kaggle

Nvidia - Deep Learning Institute

IBM Watson

Facebook AI

Affectiva

Worldquant

Intel

Amazon Alexa

AI is one of the most transformational and fastest-growing technologies of our time. Our School of Artificial Intelligence offers AI training and machine learning courses as well as programs focusing on deep learning, computer vision, natural language processing, and AI product management.

Machine learning is becoming a fundamental skill as software development is entering a new era. This path will enable you to start a career as a machine learning engineer. First learn the fundamentals of programming in Python, linear algebra, and neural networks, and then move on to core machine learning concepts.

Steps To Become A Machine Learning Engineer

Machine Learning Engineer

Deep learning is driving advances in artificial intelligence that are changing our world. To join this field, start by learning Python fundamentals and neural networks, move on to core machine learning concepts, and then apply deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment.

Steps To Become A Deep Learning Engineer

Deep Learning Engineer

Artificial intelligence is expected to be a $60 billion industry by 2025. Learn AI skills in specialized fields like computer vision, natural language processing, deep reinforcement learning, or core AI algorithms. Each of these programs covers advanced topics, building on your existing skills in programming, deep learning, and machine learning.

Artificial Intelligence

In Demand Skill Sets

Artificial Intelligence Across Industries

Programs Co-created With Artificial Intelligence Leaders.

Explore Other Schools