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Articles Home » Artificial Intelligence and Machine Learning » Become an AI and Machine Learning Specialist, Part I

Become an AI and Machine Learning Specialist, Part I

Become an AI and Machine Learning Specialist,  Part I

 

 

 

AI and machine learning are the new frontiers of technology. Build a foundational conceptual understanding of AI and machine learning, and acquire the fundamental data science skills involved in developing algorithms.
Master core concepts, including NLP and neural networks.
Build your Python skills as they apply to machine learning.
Create your own algorithms.

 

 

 

 

 

 

 

01

Artificial Intelligence Foundations: Thinking Machines with Doug Rose

1h 27m • COURSE
Computer-enhanced artificial intelligence (AI) has been around since the 1950s, but recent hardware innovations have reinvigorated the field. New sensors help machines have more accurate sight, hear sounds, and understand location. Powerful processors can help computers make complex decisions, sort through possibilities, plan outcomes, and learn from mistakes. The possibilities are thrilling; the implications are vast.

This course will introduce you to some of the key concepts behind artificial intelligence, including the differences between "strong" and "weak" AI. You'll see how AI has created questions around what it means to be intelligent and how much trust we should put in machines. Instructor Doug Rose explains the different approaches to AI, including machine learning and deep learning, and the practical uses for new AI-enhanced technologies. Plus, learn how to integrate AI with other technology, such as big data, and avoid some common pitfalls associated with programming AI.
Topics include:
  • The history of AI
  • Machine learning
  • Technical approaches to AI
  • AI in robotics
  • Integrating AI with big data
  • Avoiding pitfalls
02

Building a Recommendation System with Python Machine Learning & AI with Lillian Pierson, P.E.

1h 38m • COURSE
Discover how to use Python—and some essential machine learning concepts—to build programs that can make recommendations. In this hands-on course, Lillian Pierson, P.E. covers the different types of recommendation systems out there, and shows how to build each one. She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises. Once you're familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best.
Topics include:
  • Working with recommendation systems
  • Evaluating similarity based on correlation
  • Building a popularity-based recommender
  • Classification-based recommendations
  • Making a collaborative filtering system
  • Content-based recommender systems
  • Evaluating recommenders
03

The Essential Elements of Predictive Analytics and Data Mining with Keith McCormick

1h 28m • COURSE
A proper predictive analytics and data-mining project can involve many people and many weeks. There are also many potential errors to avoid. A "big picture" perspective is necessary to keep the project on track. This course provides that perspective through the lens of a veteran practitioner who has completed dozens of real-world projects. Keith McCormick is an independent data miner and author who specializes in predictive models and segmentation analysis, including classification trees, cluster analysis, and association rules. Here he shares his knowledge with you. Walk through each step of a typical project, from defining the problem and gathering the data and resources, to putting the solution into practice. Keith also provides an overview of CRISP-DM (the de facto data-mining methodology) and the nine laws of data mining, which will keep you focused on strategy and business value.
Topics include:
  • What makes a successful predictive analytics project?
  • Defining the problem
  • Selecting the data
  • Acquiring resources: team, budget, and SMEs
  • Dealing with missing data
  • Finding the solution
  • Putting the solution to work
  • Overview of CRISP-DM
04

Machine Learning and AI Foundations: Value Estimations with Adam Geitgey

1h 4m • COURSE
Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. For example, a website can determine how much a house is worth based on the property's location and characteristics. In this project-based course, discover how to use machine learning to build a value estimation system that can deduce the value of a home. Follow Adam Geitgey as he walks through how to use sample data to build a machine learning model, and then use that model in your own programs. Although the project featured in this course focuses on real estate, you can use the same approach to solve any kind of value estimation problem with machine learning.
Topics include:
  • Setting up the development environment
  • Building a simple home value estimator
  • Finding the best weights automatically
  • Working with large data sets efficiently
  • Training a supervised machine learning model
  • Exploring a home value data set
  • Deciding how much data is needed
  • Preparing the features
  • Training the value estimator
  • Measuring accuracy with mean absolute error
  • Improving a system
  • Using the machine learning model to make predictions
05

Machine Learning and AI Foundations: Recommendations with Adam Geitgey

58m 7s • COURSE
This project-based course shows programmers of all skill levels how to use machine learning to build programs that can make recommendations. In this course, Adam Geitgey walks you through a hands-on lab building a recommendation system that is able to suggest similar products to customers based on past products they have reviewed or purchased. The system can also identify which products are similar to each other.

Recommendation systems are a key part of almost every modern consumer website. The systems help drive customer interaction and sales by helping customers discover products and services they might not ever find themselves. The course uses the free, open source tools Python 3.5, pandas, and numpy. By the end of the course, you'll be equipped to use machine learning yourself to solve recommendation problems. What you learn can then be directly applied to your own projects.
Topics include:
  • Building a machine learning system
  • Training a machine learning system
  • Refining the accuracy of the machine learning system
  • Evaluating the recommendations received

· Posted by Superadmin on January 21 2019 · 0 Comments · 1263 Reads · Print
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