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

Become an AI and Machine Learning Specialist, Part II

Become an AI and Machine Learning Specialist, Part II

 

 

 

Branch out and extensively develop your knowledge of machine learning capabilities and techniques across tech platforms. In this learning path, you can begin to master natural language processing and deep learning, and take your AI and machine learning skills to the next level.
Expand your basic knowledge of algorithms.
Develop a more advanced understanding of how to build algorithms.
Extend your mastery of deep learning across diffrerent platforms and toolsets.

 

 

 

 

 

 

01

Building Deep Learning Applications with Keras 2.0 with Adam Geitgey

1h 24m • COURSE
Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. Instead of providing all the functionality itself, it uses either TensorFlow or Theano behind the scenes and adds a standard, simplified programming interface on top. In this course, learn how to install Keras and use it to build a simple deep learning model. Explore the many powerful pre-trained deep learning models included in Keras and how to use them. Discover how to deploy Keras models, and how to transfer data between Keras and TensorFlow so that you can take advantage of all the TensorFlow tools while using Keras. When you wrap up this course, you'll be ready to start building and deploying your own models with Keras.
Topics include:
  • What's Keras?
  • Using Keras vs. TensorFlow
  • Training a deep learning model
  • Using a pre-trained deep learning model
  • Monitoring a Keras model with TensorBoard
  • Using a trained Keras model in Google Cloud
02

Building and Deploying Deep Learning Applications with TensorFlow with Adam Geitgey

1h 46m • COURSE
TensorFlow is one of the most popular deep learning frameworks available. It's used for everything from cutting-edge machine learning research to building new features for the hottest start-ups in Silicon Valley. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to create and train a machine learning model, as well as how to leverage visualization tools to analyze and improve your model. Finally, he explains how to deploy models locally or in the cloud. When you wrap up this course, you'll be ready to start building and deploying your own models with TensorFlow.
Topics include:
  • What's TensorFlow?
  • Hardware, software, and language requirements
  • Creating a TensorFlow model
  • Training a deep learning model with TensorFlow
  • Visualizing the computational graph
  • Adding custom visualizations to TensorBoard
  • Exporting models for use with Google Cloud
03

Machine Learning & AI: Advanced Decision Trees with Keith McCormick

1h 16m • COURSE
If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. In this course, explore advanced concepts and details of decision tree algorithms. Learn about the QUEST algorithm and how it handles nominal variables, ordinal and continuous variables, and missing data. Explore the C5.0 algorithm and review some of its key features such as global pruning and winnowing. Plus, dive into a few advanced topics that apply to all decision trees, such as boosting and bagging.
Topics include:
  • Understanding QUEST functions and applications
  • C5.0 concepts and practical applications
  • Understanding information gain
  • Random forests
  • Boosting and bagging
  • Costs and priors

 
04

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
05

Amazon Web Services Machine Learning Essential Training with Lynn Langit

3h 7m • COURSE
Amazon Web Services (AWS) offers a wealth of services and tools that help data scientists leverage machine learning to craft better, more intelligent solutions. In this course, learn about patterns, services, processes, and best practices for designing and implementing machine learning using AWS. Instructor Lynn Langit takes a look at general machine learning concepts, including key machine learning algorithm types. She also examines available service types, such as AWS Machine Learning, Lex, Polly, and Rekognition, which you can use to predict image and video labels. Plus, she steps through how to work with platforms like AWS SageMaker, which includes hosted Jupyter notebooks.
Topics include:
  • How machine learning is used in analytics
  • AWS AI servers vs. platforms
  • Predicting using Polly text-to-speech
  • Predicting using Rekognition for video
  • Using Lex to build a conversational application
  • Using the AWS Machine Learning service to train, host, and predict
  • Working with MXNet in Databricks
  • Working with EMR for machine learning
06

Neural Networks and Convolutional Neural Networks Essential Training with Jonathan Fernandes

1h 19m • COURSE
Take a deep dive into neural networks and convolutional neural networks, two key concepts in the area of machine learning. In this hands-on course, instructor Jonathan Fernandes covers fundamental neural and convolutional neural network concepts. Jonathan begins by providing an introduction to the components of neural networks, discussing activation functions and backpropagation. He then looks at convolutional neural networks, explaining why they're particularly good at image recognition tasks. He also steps through how to build a neural network model using Keras. Plus, learn about VGG16, the history of the ImageNet challenge, and more.

Topics include:
Neurons and artificial neurons
Components of neural networks
Neural network visualization
Neural network implementation in Keras
Compiling and training a neural network model
Accuracy and evaluation of a neural network model
Convolutional neural networks in Keras
Enhancements to convolutional neural networks
Working with VGG16
07

Machine Learning and AI Foundations: Clustering and Association with Keith McCormick

3h 22m • COURSE
Unsupervised learning is a type of machine learning where algorithms parse unlabeled data. The focus is not on sorting data into known categories but uncovering hidden patterns. Unsupervised learning plays a big role in modern marketing segmentation, fraud detection, and market basket analysis. This course shows how to use leading machine-learning techniques—cluster analysis, anomaly detection, and association rules—to get accurate, meaningful results from big data.

Instructor Keith McCormick reviews the most common clustering algorithms: hierarchical, k-means, BIRCH, and self-organizing maps (SOM). He uses the same algorithms for anomaly detection, with additional specialized functions available in IBM SPSS Modeler. He closes the course with a review of association rules and sequence detection, and also provides some resources for learning more.

All exercises are demonstrated in IBM SPSS Modeler and IBM SPSS Statistics, but the emphasis is on concepts, not the mechanics of the software.
Topics include:
  • What is unsupervised learning?
  • Cluster and distance-based measures
  • Hierarchical cluster analysis
  • K-means cluster analysis
  • Visualizing and reporting cluster solutions
  • Cluster methods for categorical variables
  • Anomaly detection
  • Association rules
  • Sequence detection
08

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
Having a solid understanding of linear regression—a method of modeling the relationship between one dependent variable and one to several other variables—can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity

· Posted by Superadmin on January 22 2019 · 0 Comments · 845 Reads · Print
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