Python

Machine learning is changing the world and if you want to be a part of the ML revolution, this is a great place to start! In this track, you’ll learn the fundamental concepts in Machine Learning. 

Learn the art of science of Machine Learning and come away being a boss at prediction, pattern recognition and the beginnings of Deep Learning.

with Python

Machine Learning Fundamentals

In this chapter, you will be introduced to classification problems and learn how to solve them using supervised learning techniques. And you’ll apply what you learn to a political dataset, where you classify the party affiliation of United States congressmen based on their voting records.

Classification

In the previous chapter, you used image and political datasets to predict binary and multiclass outcomes. But what if your problem requires a continuous outcome? Regression is best suited to solving such problems. You will learn about fundamental concepts in regression and apply them to predict the life expectancy in a given country using Gapminder data.

Regression

Having trained your model, your next task is to evaluate its performance. In this chapter, you will learn about some of the other metrics available in scikit-learn that will allow you to assess your model's performance in a more nuanced manner. Next, learn to optimize your classification and regression models using hyperparameter tuning.

Fine-tuning your model

This chapter introduces pipelines, and how scikit-learn allows for transformers and estimators to be chained together and used as a single unit. Preprocessing techniques will be introduced as a way to enhance model performance, and pipelines will tie together concepts from previous chapters.

Preprocessing and pipelines

Automobile miles per gallon

Boston housing

Diabetes

Gapminder

US Congressional Voting Records (1984)

White wine quality

Red wine quality

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 business elsewhere? Is a particular email spam? In this course, you'll learn how to use Python to perform supervised learning, an essential component of machine learning. You'll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets. You'll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

Statistical Thinking in Python (Part 1)

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

Supervised Learning with scikit-learn

Machine Learning

Learn how to discover the underlying groups (or "clusters") in a dataset.  By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.

Clustering for dataset exploration

In this chapter, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE.  Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy.  t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.

Visualization with hierarchical clustering and t-SNE

Dimension reduction summarizes a dataset using its common occuring patterns.  In this chapter, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA").  PCA is often used before supervised learning to improve model performance and generalization.  It can also be useful for unsupervised learning.  For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!

Decorrelating your data and dimension reduction

In this chapter, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts.  For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns.  You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!

Discovering interpretable features

Company stock price movements

Eurovision 2016

Fish measurements

Grains

LCD digits

Musical artists

Wikipedia articles

Wine

Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you'll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and scipy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Unsupervised Learning in Python

In this chapter you will learn the basics of applying logistic regression and support vector machines (SVMs) to classification problems. You'll use the <code>scikit-learn</code> library to fit classification models to real data.

Applying logistic regression and SVM

In this chapter you will discover the conceptual framework behind logistic regression and SVMs. This will let you delve deeper into the inner workings of these models.

Loss functions

In this chapter you will delve into the details of logistic regression. You'll learn all about regularization and how to interpret model output.

Logistic regression

In this chapter you will learn all about the details of support vector machines. You'll learn about tuning hyperparameters for these models and using kernels to fit non-linear decision boundaries.

Support Vector Machines

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 find out what really makes them tick. At the end of this course you'll know how to train, test, and tune these linear classifiers in Python. You'll also have a conceptual foundation for understanding many other machine learning algorithms.

In this course you will learn the details of linear classifiers like logistic regression and SVM.

Linear Classifiers in Python

In this chapter, you'll be introduced to the problem you'll be solving in this course. How do you accurately classify line-items in a school budget based on what that money is being used for? You will explore the raw text and numeric values in the dataset, both quantitatively and visually. And you'll learn how to measure success when trying to predict class labels for each row of the dataset.

Exploring the raw data

In this chapter, you'll build a first-pass model. You'll use numeric data only to train the model. Spoiler alert - throwing out all of the text data is bad for performance! But you'll learn how to format your predictions. Then, you'll be introduced to natural language processing (NLP) in order to start working with the large amounts of text in the data.

Creating a simple first model

Here, you'll improve on your benchmark model using pipelines. Because the budget consists of both text and numeric data, you'll learn to how build pipielines that process multiple types of data. You'll also explore how the flexibility of the pipeline workflow makes testing different approaches efficient, even in complicated problems like this one!

Improving your model

In this chapter, you will learn the tricks used by the competition winner, and implement them yourself using scikit-learn. Enjoy!

Learning from the experts

Data science isn't just for predicting ad-clicks-it's also useful for social impact! This course is a case study from a machine learning competition on DrivenData. You'll explore a problem related to school district budgeting. By building a model to automatically classify items in a school's budget, it makes it easier and faster for schools to compare their spending with other schools. In this course, you'll begin by building a baseline model that is a simple, first-pass approach. In particular, you'll do some natural language processing to prepare the budgets for modeling. Next, you'll have the opportunity to try your own techniques and see how they compare to participants from the competition. Finally, you'll see how the winner was able to combine a number of expert techniques to build the most accurate model.

This course is a case study from a machine learning competition on DrivenData. Learn how to build a model to automatically classify items in a school budget.

Case Study: School Budgeting with Machine Learning in Python

Learn how to build a model to automatically classify items in a school budget.

Case Studies

In this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today. You'll build simple neural networks and generate predictions with them.

Basics of deep learning and neural networks

Learn how to optimize the predictions generated by your neural networks. You'll use a method called backward propagation, which is one of the most important techniques in deep learning. Understanding how it works will give you a strong foundation to build on in the second half of the course.

Optimizing a neural network with backward propagation

In this chapter, you'll use the Keras library to build deep learning models for both regression and classification. You'll learn about the Specify-Compile-Fit workflow that you can use to make predictions, and by the end of the chapter, you'll have all the tools necessary to build deep neural networks.

Building deep learning models with keras

Learn how to optimize your deep learning models in Keras. Start by learning how to validate your models, then understand the concept of model capacity, and finally, experiment with wider and deeper networks. 

Fine-tuning keras models

Hourly wages

MNIST

Titanic

Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

Introduction to Deep Learning in Python

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.

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Machine Learning Fundamentals with Python

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Join over 8,160,000 learners and start Machine Learning Fundamentals with Python today!