We recommend the Machine Learning tracks
Machine Learning Fundamentals
Discover the machine learning fundamentals and explore how machine learning is changing the world. Join the ML revolution today! If you’re new to the discipline, this is an ideal place to start. You’ll cover the machine learning basics with Python, starting with supervised learning with the scikit-learn library. You’ll also learn how to cluster, transform, visualize, and extra insights from data using unsupervised learning and scipy. As you progress, you’ll explore linear classifiers for machine learning in Python, including logistics regression and support vector machines. You’ll finish the track by covering the fundamentals of neural networks and deep learning models using PyTorch. By the time you’re finished, you’ll understand the essential machine learning concepts and be able to apply the fundamentals of machine learning with Python.
Machine Learning Scientist
Master the essential Python skills to land a job as a machine learning scientist! With this track, you'll gain a comprehensive introduction to machine learning in Python. You’ll augment your existing Python programming skill set with the tools needed to perform supervised, unsupervised, and deep learning. You'll learn how to process data for features, train your models, assess performance, and tune parameters for better performance. This track also covers topics including tree-based machine learning models, cluster analysis, preprocessing for machine learning, and more. By the time you finish, you’ll have the confidence to use Python for machine learning, working with real data sets, linear classifiers, gradient boosting, and more. In the process, you'll get an introduction to natural language processing, image processing, and popular Python machine learning packages such as scikit-learn, Spark, and Keras.
Keras Fundamentals
In this track, you'll expand your deep learning knowledge and take your machine learning skills to the next level. Working with Keras, you’ll learn about neural networks, the deep learning model workflows, and how to optimize your models. Throughout the track, you'll use deep learning techniques to solve real-world challenges, such as predicting housing prices, and building neural networks to model images and text. By the end of the track, you'll be ready to use Keras to train and test complex, multi-output networks and dive deeper into deep learning.
Natural Language Processing
The majority of data is unstructured. This includes information recorded in books, online articles, and audio files. In this track, you’ll gain the core Natural Language Processing (NLP) skills you need to convert that data into valuable insights—from learning how to automatically transcribe TED talks through to identifying whether a movie review is positive or negative. Along the way, you’ll be introduced to popular NLP Python libraries, including NLTK, scikit-learn, spaCy, and SpeechRecognition. By the end of the track, you'll be ready to transcribe audio files and understand how to extract insights from real-world sources, including Wikipedia articles, online review sites, and data from a flight booking system.
Supervised Machine Learning
Master the fundamentals of supervised machine learning and discover how to make predictions using labeled data. Join the ML revolution today! If you’re new to machine learning, or want to specialize in supervised machine learning, this is an ideal place to start. You’ll start by learning about and implementing core supervised learning models, such as K-Nearest Neighbors (KNN), Logistic Regression, Linear Regression, Support Vector Machines (SVMs), and tree-based models with the popular scikit-learn library. You’ll also discover how to use state-of-the-art algorithms like XGBoost to efficiently boost modelling performance on tabular datasets. To get the most out of your models, you’ll learn about different hyperparameter tuning techniques and how to decide which technique to use for your use case. You’ll finish the track by bringing your knowledge of these diverse models together to learn about ensemble learning, where different models are combined to improve performance and solve more complex problems. By the time you’re finished, you’ll have mastered the essential supervised machine learning concepts and be able to apply them in Python.
Machine Learning Engineer
Step into the cutting-edge field of machine learning engineering with this comprehensive track designed for aspiring professionals. This program teaches you everything you need to know about model deployment, operations, monitoring, and maintenance. In this track, you will learn the fundamentals of MLOps. You will work interactively with key technologies like Python, Docker, and MLflow. You will learn in detail about concepts such as CI/CD, deployment strategies, or concept drift. The track includes interactive courses and real-world projects that help you facilitate the skills learned. Upon completing this track, you'll emerge as a well-rounded machine learning engineer with all the skills required for a junior machine learning engineer role. Note: Prior knowledge of concepts, including data manipulation, training, and evaluating machine learning models using Python, is expected from learners who enroll in this track.
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