Become an AI and Machine Learning Specialist, Part I
Posted by Superadmin on January 21 2019 11:02:46

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

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

 

 

 



01 Introduction
001 Welcome



02. What Is Artificial Intelligence?
002. Define general intelligence
003.The history of AI
004.Strong vs. weak AI
005.Plan AI



03. The Rise of Machine Learning
006.Machine learning
007.Artificial neural networks
008.Perceptrons



04. Finding the Right Approach
009.Match patterns
010Data vs. reasoning
011.Unsupervised learning
012.Backpropagation
013.Regression



05. Common AI Programs
014.Robotics
015.Natural language processing
016.The Internet of Things



06. Mixing with Other Technologies
017.Big data
018.Data science



07. Avoiding Pitfalls
019.Pitfalls
020.Next steps



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


1. Introduction
01. Welcome
02. Using the exercise files



2.Simple Approaches to Recommendation Systems
03. Introducing core concepts of recommendation systems
04. Popularity-based recommenders
05. Evaluating similarity based on correlation



3. Machine Learning Recommendation Systems
06. Classification-based collaborative filtering
07. Model-based collaborative filtering systems
08. Content-based recommender systems
09. Evaluating recommendation systems



4. Conclusion
10. Next steps



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


1. Introduction
01. Welcome
02. What you should know before watching this course



2. What Is Data Mining and Predictive Analytics
03. Introduction
04. A definition of data mining
05. What's data mining and predictive analytics
06. What are the essential elements



3.Problem Definition
07. Introduction
08. Determine the business objective
09. Identify an intervention strategy
10. Estimate the return on investment
11. Program management



4.Data Requirements
12. Introduction
13. Customer footprint
14. Flat file
15. Understand your target
16. Select the data for modeling
17. Understand integration
18. Understand data construction



5.Resources You will Need
19. Introduction
20. Understand data mining algorithms
21. Assess team requirements
22. Budget time
23. Work with subject matter experts



6. Problems You will Face
24. Introduction
25. Deal with missing data
26. Resolve organizational resistance
27. Degrade models



7.Finding the Solution
28. Introduction
29. Search the solution space
30. Unexpected results
31. Trial and error
32. Construct proof



8. Putting the Solution to Work
33. Introduction
34. Understand propensity
35. Understand metamodeling
36. Understand reproducibility
37. Master documentation
38. Time to deploy



9.CRISP-DM and the Nine Laws
39. Introduction
40. Understanding CRISP-DM
41. Understand laws 1 and 2
42. Understand law 3
43. Understand laws 4 and 5
44. Understand laws 6, 7, and 8
45. Understand law 9



10. Conclusion
46. Next steps



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


SECTION 1 INTRODUCTION
01_01-Welcome
01_02-What you should know
01_03-Using the exercise files
01_04-Set up the development environment



SECTION 2 WHAT IS MACHINE LEARNING AND VALUE PREDICTION
02_01-What is machine learning
02_02-Supervised machine learning for value prediction
02_03-Build a simple home value estimator
02_03-Build a simple home value estimator
02_05-Cool uses of value prediction



SECTION 3 AN OVERVIEW OF BUILDING A MACHINE LEARNING SYSTEM
03_01-Introduction to NumPy, scikitlearn, and pandas
03_02-Think in vectors_ How to work with large data sets efficiently
03_03-The basic workflow for training a supervised machine learning model
03_04-Gradient boosting_ A versatile machine learning algorithm



SECTION 4 TRAINING DATA
04_01-Explore a home value data set
04_02-Standard conventions for naming training data
04_03-Decide how much data you need



SECTION 5 FEATURES
05_01-Feature engineering
05_02-Choose the best features for home value prediction
05_03-Use as few features as possible_ The curse of dimensionality



SECTION 6 CODING OUR SYSTEM
06_01-Prepare the features
06_02-Training vs. testing data
06_03-Train the value estimator
06_04-Measure accuracy with mean absolute error



SECTION 7 IMPROVING OUR SYSTEM
07_01-Overfitting and underfitting
07_02-The brute force solution_ Grid search
07_03-Feature selection



SECTION 8 USING THE ESTIMATOR IN A REAL-WORLD PROGRAM
08_01-Predict values for new data
08_01-Predict values for new data



SECTION 9 CONCLUSION
09_01-Wrapup



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


0. INTRODUCTION
001 Welcome
002 What you should know
003 Using the exercise files
004 Set up environment



1. The Basics of Making Recommendations
005 What is a recommendation system_
006 What can you do with recommendation systems_
007 Cool uses of recommendation systems



2. Ways of Making Recommendations
008 Content-based recommendations - Recommending based on product attributes
009 Collaborative filtering - Recommending based on similar users



3. Getting to Know Our Tools
010 Introduction to NumPy, SciPy, and pandas
011 Think in vectors - How to work with large data sets efficiently



4. Building the Framework for Our Recommendation System
012 Explore our product recommendation data set
013 Represent product reviews as a matrix
014 Recommend by predicting missing user ratings
015 A simple way to predict missing user ratings



5. Collaborative Filtering with Matrix Factorization
016 Latent representations of users and products
017 Code the recommendation system
018 How matrix factorization works
019 Use latent representations to find similar products



6. Testing Our System
020 Explore our system's recommendations
021 Use regularization
022 Measure recommendation accuracy



7. Using the Recommendation System in a Real World Program
023 Make recommendations for existing users
024 How to handle first-time users
025 Find similar products



CONCLUSION
026 Wrap up