Master the essential skills to land a job as a machine learning scientist! You'll augment your Python programming skill set with the toolbox 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. In the process, you'll get an introduction to natural language processing, image processing, and popular libraries such as Spark and Keras.
In this track, you'll expand your deep learning knowledge and take your machine learning skills to the next level. Working with Keras and PyTorch, you’ll learn about neural networks, the deep learning model workflows, and how to optimize your models. You'll then use TensorFlow to build linear regression models and neural networks. Throughout the track, you'll use machine learning techniques to solve real-world challenges, such as predicting housing prices, building a neural network to predict handwritten numbers, and identify forged banknotes. 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.
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. You’ll start this track by learning how to identify words and extract topics in text before building your very own chatbot that transforms human language into actionable instructions. 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.
Further your Natural Language Processing (NLP) skills and master the machine learning techniques needed to extract insights from data. In this track, you'll learn how to create Recurrent Neural Networks (RNN), build models to translate language, and autocomplete sentences like Gmail using neural translation and seq2seq models. Through interactive exercises, you'll use the scikit-learn, TensorFlow, Keras, and NLTK libraries. Then, you’ll apply your skills to real-world data, including scripts from The Big Bang Theory, English and French vocabulary, and the works of Shakespeare. Start this track and gain the machine learning skills you need to enhance your NLP skills in Python.