Duration
21 hours (usually 3 days including breaks)
Requirements
- R programming experience
- An understanding of machine learning concepts
Overview
In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.
By the end of this training, participants will be able to:
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
Introduction
Setting up the R Development Environment
Deep Learning vs Neural Network vs Machine Learning
Building an Unsupervised Learning Model
Case Study: Predicting an Outcome Using Existing Data
Preparing Test and Training Data Sets For Analysis
Clustering Data
Classifying Data
Visualizing Data
Evaluating the Performance of a Model
Iterating Through Model Parameters
Hyper-parameter Tuning
Integrating a Model with a Real-World Application
Deploying a Machine Learning Application
Troubleshooting
Summary and Conclusion
Duration
14 hours (usually 2 days including breaks)
Requirements
- Java or .Net programming experience
- An understanding of machine learning concepts
Overview
Encog is an open-source machine learning framework for Java and .Net.
In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.
By the end of this training, participants will be able to:
- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
To request a customized course outline for this training, please contact us.
Duration
21 hours (usually 3 days including breaks)
Requirements
- Python programming experience
- An understanding of basic principles of machine learning
Audience
- Developers
- Analysts
- Data scientists
Overview
In this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.
By the end of this training, participants will be able to:
- Implement machine learning algorithms and techniques for solving complex problems.
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
- Push Python algorithms to their maximum potential.
- Use libraries and packages such as NumPy and Theano.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
Introduction
Describing the Structure of Unlabled Data
- Unsupervised Machine Learning
Recognizing, Clustering and Generating Images, Video Sequences and Motion-capture Data
- Deep Belief Networks (DBNs)
Reconstructing the Original Input Data from a Corrupted (Noisy) Version
- Feature Selection and Extraction
- Stacked Denoising Auto-encoders
Analyzing Visual Images
- Convolutional Neural Networks
Gaining a Better Understanding of the Structure of Data
Understanding Text Data
Building Highly Accurate Predictive Models
- Improving Machine Learning Results
- Ensemble Methods
Summary and Conclusion
