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Introduction to Machine Learning with TensorFlow

Nanodegree Program

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.

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.

Intermediate

2 months

Real-world Projects

Completion Certificate

Last Updated August 14, 2023

Skills you'll learn:
Naive bayes classifiers • Gaussian mixture models • Model evaluation • Support vector machines
Prerequisites:
Basic descriptive statistics • Python for data science • Basic probability

Courses In This Program

Course 1 1 hour

Introduction to Machine Learning

Course 2 3 weeks

Supervised Learning

In this course, you'll learn about different types of supervised learning and how to use them to solve real-world problems.

Course 3 3 weeks

Introduction to Neural Networks with TensorFlow

Learn the fundamentals of neural networks with Python and TensorFlow, and then use your new skills to create your own image classifier—an application that will first train a deep learning model on a dataset of images and then use the trained model to classify new images.

Course 4 4 weeks

Unsupervised Learning

In this course, you'll learn how to apply unsupervised learning to solve real-world problems.

Taught By The Best

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Josh Bernhard

Staff Data Scientist

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

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Michael Virgo

Instructor

After beginning his career in business, Michael utilized Udacity Nanodegree programs to build his technical skills, eventually becoming a Self-Driving Car Engineer at Udacity before switching roles to work on curriculum development for a variety of AI and Autonomous Systems programs.

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Mat Leonard

Content Developer

Mat is a former physicist, research neuroscientist, and data scientist. He did his PhD and Postdoctoral Fellowship at the University of California, Berkeley.

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Andrew Paster

Instructor

Andrew has an engineering degree from Yale, and has used his data science skills to build a jewelry business from the ground up. He has additionally created courses for Udacity's Self-Driving Car Engineer Nanodegree program.

Photo of Jennifer Staab

Jennifer Staab

Instructor

Jennifer has a PhD in Computer Science and a Masters in Biostatistics; she was a professor at Florida Polytechnic University. She previously worked at RTI International and United Therapeutics as a statistician and computer scientist.

Photo of Dan Romuald Mbanga

Dan Romuald Mbanga

Instructor

Dan leads Amazon AI's Business Development efforts for Machine Learning Services. Day to day, he works with customers—from startups to enterprises—to ensure they are successful at building and deploying models on Amazon SageMaker.

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Cezanne Camacho

Curriculum Lead

Cezanne is an expert in computer vision with a Masters in Electrical Engineering from Stanford University. As a former researcher in genomics and biomedical imaging, she's applied computer vision and deep learning to medical diagnostic applications.

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Sean Carrell

Instructor

Sean Carrell is a former research mathematician specializing in Algebraic Combinatorics. He completed his PhD and Postdoctoral Fellowship at the University of Waterloo, Canada.

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Jay Alammar

Instructor

Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.

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Luis Serrano

Instructor

Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

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Juan Delgado

Content Developer

Juan is a computational physicist with a Masters in Astronomy. He is finishing his PhD in Biophysics. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.

Ratings & Reviews

Average Rating: 4.6 Stars

(256 Reviews)

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