Starts Monday: Beginner Python & Math for Data Science Explore Course

Accredited Professional Development Course

Machine Learning and Artificial Intelligence Principles

Offered Live Online Only

From robotics, speech recognition, and analytics to finance and social network analysis, machine learning comprises one of the most useful scientific toolsets of our age. This course provides an overview of the core principles of machine learning and artificial intelligence using a hands-on, project-based curriculum. There is an intense focus on implementing popular machine learning algorithms to solve real problems using real data. So get ready to learn work hard and complete 5 portfolio-building projects in 5 weeks.

Who the course is designed for:

Individuals working in any number of data-intensive fields including consulting, finance, information technology, healthcare, and logistics, as well as recent college graduates and entrepreneurs interested or specializing in these fields and others like them.

Outcomes

  • An understanding of the basic principles of machine learning and artificial intelligence from both an intuitive and practical level.
  • An understanding of common feature design principles for image and text data.
  • An understanding of how to use popular machine learning and deep learning software packages in Python, as well as how to implement several popular machine learning algorithms (Linear/Logistic Regression; KMeans Clustering) from scratch.
  • Extensive experience applying machine learning algorithms to real data sets.
Have questions? Get answers to frequently asked questions. FAQs

What you'll receive upon completion:

  • Certificate of completion
  • Certificate link and instructions on how to add to your LinkedIn profile
  • 2.7 Continuing Education Units

Dates & Instructors

Live Online
$1,750

Machine Learning and Artificial Intelligence Principles

July 30 to August 29

Mondays and Wednesdays

6:30 - 9:30pm Pacific Time

Nathan grossman
Nathan Grossman
Instructor

As a former engineer, Nathan enjoys data science because it allows him to apply the mathematical skill set he developed designing algorithms for individual products to optimize the behavior of entire organizations. Nathan began his career developing signal processing algorithms for wireless communications at Qualcomm. He subsequently worked in data analytics for intellectual property applications, and in data science for telecom applications. Nathan is currently a data scientist at Wells Fargo, working on machine learning for FinTech applications. He holds a BS in Mechanical Engineering from the University of Illinois, an MS in Electrical Engineering from the University of Michigan, and an MBA from the University of Pennsylvania’s Wharton School, as well as certificates in data science and data analytics from UC Berkeley and UC Santa Cruz. When not working, Nathan enjoys skiing, cycling, playing tennis and spending time with his wife and three sons.

Enroll Sign Up for a Free Sample Class

Prerequisites

Because there will not be any introductory Python material taught in this course, students should have firm knowledge of that programming environment. If you’re not comfortable coding in Python, please keep working at it and join us in the (hopefully very near) future!

Additionally, students should have a basic understanding of vector and matrix algebra (how to add and multiply vectors/matrices), as well as basic understanding of the notion of a mathematical function (e.g., understanding what f(x)=x^2 or f(x) = sin(x) means). Knowledge of basic calculus and linear algebra is helpful but not required (e.g., how to take derivatives, what a linear system of equations is, etc.). A quick refresher on these topics will be provided. Note: Knowledge of statistics is not required for this course.

Considering our immersive data science bootcamp?

Professional development alumni can apply the amount of tuition paid for one part-time course towards enrollment in an upcoming bootcamp upon admittance.

Course Structure & Syllabus

Week 1
Introduction

Overview of both machine learning and the course itself before jumping right into the first project.

  • What kinds of things can you build with machine learning tools?
  • How does machine learning work? (The 5-minute elevator pitch edition.)
  • Predictive models our basic building blocks.
  • Getting your hands dirty with Python.
  • Introduction to regression and classification.
Week 2
Learning to Vet Real World Models

With cross-validation, build models using real data.

  • Knowledge-driven feature design for classification with examples from computer vision (object/face detection and recognition) and text mining.
  • Using cross-validation to build robust classification models with healthcare data.
Week 3
Understanding Optimization

Go "under the hood" of gradient descent, the most popular optimization algorithm in machine learning.

  • Using calculus to build useful algorithms (calculus defined optimality and solving the least squares problem).
  • Brief primer on stochastic gradient descent.
  • Understanding the theory behind linear regression.
  • Understanding Logistic Regression for 2 class and multi-class classification.
Week 4
Making Sense of Data – Clustering/Decomposition & Nonlinear Regression/Classification

Use advanced machine learning tools for supervised and unsupervised learning.

  • Tools for unlabeled datasets: K-means clustering and extensions.
  • Tools for high dimensional data: principal component analysis.
  • Decision Trees and Forests of Trees (Random Forests).
Week 5
Deep Learning

Introduction to deep learning in Python using Keras for visual and natural language processing applications.

  • Feed-forward neural networks and backpropagation.
  • Convolutional Neural Networks - Deep Learning for Computer Vision.
  • LSTM Networks - Deep Learning for NLP.
Other Information
Resources

Students should come to class with a laptop with Python installed. Using an Integrated Development Environment for Python (like PyCharm or Eclipse) is highly recommended for debugging purposes.

We will use publicly available machine learning libraries written for Python including:

  • Scikit-learn general purpose machine learning library
  • Keras deep learning Python library

Publicly available datasets from the following sources will also be used:

  • The UCI machine learning repository
  • Kaggle, a data science competition website
  • Yelp data challenge

Projects

Exploring Deep Learning Software

Deep learning, or neural networks, are popular because they can scale with enormous datasets. Students get hands-on experience using a popular software package to perform a common deep learning task called general object detection.

Handwritten Digit Recognition

Handwritten digit recognition is a classic machine learning problem with popular solutions implemented in ATMs, mobile banking apps (to automatically read checks), and postal services (to automatically sort mail). Students implement a multi-class classification scheme to perform digit recognition using real-world datasets.

Building a Face Detection System

When using a smartphone to take pictures of other people, built-in face detection algorithms locate the faces within the camera viewfinder (usually by putting little squares around each one), so the camera knows where to focus the image. We will explore how the core piece of this machine learning algorithm works and students will get hands-on experience completing a prototype face detection system.

Sentiment Analysis on Text Data

Gauging the general population’s feelings about a product, company, or politician (referred to as sentiment analysis) is getting easier thanks to massive public datasets generated by social media sites like Twitter. Students practice performing sentiment analysis on real data to understand how it’s done.

Preventative Medicine and Healthcare Logistics

Can we predict who needs preventative care that could drastically improve – if not save – lives? Students mine a real-world dataset to determine individuals who most likely require preventative healthcare in order to avert catastrophic medical costs and consequences.

Live Online Interactive Learning

Learn from world-class data science practitioners.

Our Live Online instructors bring deep industry experience from a broad range of industries and companies including Viacom, Spotify, and Capital One Labs. You’ll have an Instructor and Assistant Instructor to support you throughout your learning process.

Interact with instructors and classmates in real-time.

This course is truly live, which means you can interact with the instructors and your fellow students in real-time. Stay engaged by asking questions and participating in polls and conversations, and join your course Slack channel for additional support, communication, and collaboration.

Learn online without sacrificing the value of live instruction.

The world is your classroom. Log in from wherever you are and gain access to live, interactive data science instruction that will push your career further in the right direction. In case you have to miss a class, you can access all recordings 24/7 to stay caught up and refer back.

Earn CEUs for accredited courses.

Not only will you walk away with new data science skills and knowledge, you’ll also earn up to 3.3 Continuing Education Units (CEUs). Our courses are accredited by ACCET, who requires we maintain high standards in areas such as quality of instruction and positive student feedback.

Register for a live online sample class

July 18, 2018

6:30 - 7:30pm Pacific Time

Our 1-hour Live Online sample class serves as an excellent way to preview the Live Online format and experience.

Nathan Grossman, instructor of the Live Online Machine Learning & AI Principles course, will cover a few sample topics in the class followed by Q&A.

  1. Introduction to plotting in Python
  2. Using a linear regressor on a linear dataset
  3. Using a linear regressor on a nonlinear dataset

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FAQs

Have more questions? No problem. Schedule a chat with admissions