Web resources and competitions
by Krishna Choppella, Dr. Uday Kamath, Boštjan Kaluža, Jennifer L. Reese, Richard M
Machine Learning: End-to-End guide for Java developers
- Machine Learning: End-to-End guide for Java developers
- Table of Contents
- Machine Learning: End-to-End guide for Java developers
- Credits
- Preface
- 1. Module 1
- 1. Getting Started with Data Science
- Problems solved using data science
- Understanding the data science problem - solving approach
- Acquiring data for an application
- The importance and process of cleaning data
- Visualizing data to enhance understanding
- The use of statistical methods in data science
- Machine learning applied to data science
- Using neural networks in data science
- Deep learning approaches
- Performing text analysis
- Visual and audio analysis
- Improving application performance using parallel techniques
- Assembling the pieces
- Summary
- 2. Data Acquisition
- 3. Data Cleaning
- 4. Data Visualization
- 5. Statistical Data Analysis Techniques
- 6. Machine Learning
- 7. Neural Networks
- 8. Deep Learning
- 9. Text Analysis
- 10. Visual and Audio Analysis
- 11. Mathematical and Parallel Techniques for Data Analysis
- 12. Bringing It All Together
- 1. Getting Started with Data Science
- 2. Module 2
- 1. Applied Machine Learning Quick Start
- 2. Java Libraries and Platforms for Machine Learning
- 3. Basic Algorithms – Classification, Regression, and Clustering
- 4. Customer Relationship Prediction with Ensembles
- 5. Affinity Analysis
- 6. Recommendation Engine with Apache Mahout
- 7. Fraud and Anomaly Detection
- 8. Image Recognition with Deeplearning4j
- 9. Activity Recognition with Mobile Phone Sensors
- 10. Text Mining with Mallet – Topic Modeling and Spam Detection
- 11. What is Next?
- A. References
- 3. Module 3
- 1. Machine Learning Review
- Machine learning – history and definition
- What is not machine learning?
- Machine learning – concepts and terminology
- Machine learning – types and subtypes
- Datasets used in machine learning
- Machine learning applications
- Practical issues in machine learning
- Machine learning – roles and process
- Machine learning – tools and datasets
- Summary
- 2. Practical Approach to Real-World Supervised Learning
- Formal description and notation
- Data transformation and preprocessing
- Feature relevance analysis and dimensionality reduction
- Model building
- Model assessment, evaluation, and comparisons
- Case Study – Horse Colic Classification
- Summary
- References
- 3. Unsupervised Machine Learning Techniques
- Issues in common with supervised learning
- Issues specific to unsupervised learning
- Feature analysis and dimensionality reduction
- Clustering
- Outlier or anomaly detection
- Real-world case study
- Summary
- References
- 4. Semi-Supervised and Active Learning
- Semi-supervised learning
- Active learning
- Case study in active learning
- Summary
- References
- 5. Real-Time Stream Machine Learning
- Assumptions and mathematical notations
- Basic stream processing and computational techniques
- Concept drift and drift detection
- Incremental supervised learning
- Incremental unsupervised learning using clustering
- Modeling techniques
- Partition based
- Hierarchical based and micro clustering
- Density based
- Grid based
- Validation and evaluation techniques
- Key issues in stream cluster evaluation
- Evaluation measures
- Cluster Mapping Measures (CMM)
- V-Measure
- Other external measures
- Unsupervised learning using outlier detection
- Case study in stream learning
- Summary
- References
- 6. Probabilistic Graph Modeling
- Probability revisited
- Graph concepts
- Bayesian networks
- Representation
- Inference
- Learning
- Learning parameters
- Learning structures
- Measures to evaluate structures
- Methods for learning structures
- Constraint-based techniques
- Search and score-based techniques
- 7. Deep Learning
- Multi-layer feed-forward neural network
- Inputs, neurons, activation function, and mathematical notation
- Multi-layered neural network
- Structure and mathematical notations
- Activation functions in NN
- Training neural network
- Empirical risk minimization
- Parameter initialization
- Loss function
- Gradients
- Feed forward and backpropagation
- How does it work?
- Regularization
- L2 regularization
- L1 regularization
- Limitations of neural networks
- Deep learning
- Building blocks for deep learning
- Rectified linear activation function
- Restricted Boltzmann Machines
- Autoencoders
- Unsupervised pre-training and supervised fine-tuning
- Deep feed-forward NN
- Deep Autoencoders
- Deep Belief Networks
- Deep learning with dropouts
- Sparse coding
- Convolutional Neural Network
- CNN Layers
- Recurrent Neural Networks
- Building blocks for deep learning
- Case study
- Summary
- References
- 8. Text Mining and Natural Language Processing
- NLP, subfields, and tasks
- Text categorization
- Part-of-speech tagging (POS tagging)
- Text clustering
- Information extraction and named entity recognition
- Sentiment analysis and opinion mining
- Coreference resolution
- Word sense disambiguation
- Machine translation
- Semantic reasoning and inferencing
- Text summarization
- Automating question and answers
- Issues with mining unstructured data
- Text processing components and transformations
- Topics in text mining
- Tools and usage
- Summary
- References
- NLP, subfields, and tasks
- 9. Big Data Machine Learning – The Final Frontier
- What are the characteristics of Big Data?
- Big Data Machine Learning
- Batch Big Data Machine Learning
- Case study
- Business problem
- Machine Learning mapping
- Data collection
- Data sampling and transformation
- Spark MLlib as Big Data Machine Learning platform
- A. Linear Algebra
- B. Probability
- D. Bibliography
- Index
- Empirical risk minimization
- Multi-layer feed-forward neural network
- Modeling techniques
- 1. Machine Learning Review
In this section, we'll review where to find additional resources for learning, discussing, presenting, or sharpening our data science skills.
One of the most well-known repositories of machine learning datasets is hosted by the University of California, Irvine. The UCI repository contains over 300 datasets covering a wide variety of challenges, including poker, movies, wine quality, activity recognition, stocks, taxi service trajectories, advertisements, and many others. Each dataset is usually equipped with a research paper where the dataset was used, which can give you a hint on how to start and what is the prediction baseline.
The UCI machine learning repository can be accessed at https://archive.ics.uci.edu, as follows:
Another well-maintained collection by Xiaming Chen is hosted on GitHub:
https://github.com/caesar0301/awesome-public-datasets
The Awesome Public Datasets repository maintains links to more than 400 data sources from a variety of domains, ranging from agriculture, biology, economics, psychology, museums, and transportation. Datasets, specifically targeting machine learning, are collected under the image processing, machine learning, and data challenges sections.
Learning how to become a data scientist has became much more accessible due to the availability of online courses. The following is a list of free resources to learn different skills online:
- Online courses for learning Java:
- Udemy: Learn Java Programming From Scratch at https://www.udemy.com/learn-java-programming-from-scratch
- Udemy: Java Tutorial for Complete Beginners at https://www.udemy.com/java-tutorial
- LearnJAvaOnline.org: Interactive Java tutorial at http://www.learnjavaonline.org/
- Online courses to learn more about machine learning:
- Coursera: Machine Learning (Stanford) by Andrew Ng: This teaches you the math behind many the machine learning algorithms, explains how they work, and explores why they make sense at https://www.coursera.org/learn/machine-learning.
- Statistics 110 (Harvard) by Joe Biltzstein: This course lets you discover the probability of related terms that you will hear many times in your data science journey. Lectures are available on YouTube at http://projects.iq.harvard.edu/stat110/youtube.
- Data Science CS109 (Harvard) by John A. Paulson: This is a hands-on course where you'll learn about Python libraries for data science, as well as how to handle machine-learning algorithms at http://cs109.github.io/2015/.
The best way to sharpen your knowledge is to work on real problems; and if you want to build a proven portfolio of your projects, machine learning competitions are a viable place to start:
- Kaggle: This is the number one competition platform, hosting a wide variety of challenges with large prizes, strong data science community, and lots of helpful resources. You can check it out at https://www.kaggle.com/.
- CrowdANALYTIX: This is a crowdsourced data analytics service that is focused on the life sciences and financial services industries at https://www.crowdanalytix.com.
- DrivenData: This hosts data science competitions for social good at http://www.drivendata.org/.
In addition to online courses and competitions, there are numerous websites and blogs publishing the latest developments in the data science community, their experience in attacking different problems, or just best practices. Some good starting points are as follows:
- KDnuggets: This is the de facto portal for data mining, analytics, big data, and data science, covering the latest news, stories, events, and other relevant issues at http://www.kdnuggets.com/.
- Machine learning mastery: This is an introductory-level blog with practical advice and pointers where to start. Check it out at http://machinelearningmastery.com/.
- Data Science Central: This consists of practical community articles on a variety of topics, algorithms, caches, and business cases at http://www.datasciencecentral.com/.
- Data Mining Research by Sandro Saitta at http://www.dataminingblog.com/.
- Data Mining: Text Mining, Visualization and Social Media by Matthew Hurst, covering interesting text and web mining topics, frequently with applications to Bing and Microsoft at http://datamining.typepad.com/data_mining/.
- Geeking with Greg by Greg Linden, inventor of Amazon recommendation engine and Internet entrepreneur. You can check it out at http://glinden.blogspot.si/.
- DSGuide: This is a collection of over 150 data science blogs at http://dsguide.biz/reader/sources.
The following are a few top-tier academic conferences with the latest algorithms:
- Knowledge Discovery in Databases (KDD)
- Computer Vision and Pattern Recognition (CVPR)
- Annual Conference on Neural Information Processing Systems (NIPS)
- International Conference on Machine Learning (ICML)
- IEEE International Conference on Data Mining (ICDM)
- International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)
- International Joint Conference on Artificial Intelligence (IJCAI)
Some business conferences are as follows:
- O'Reilly Strata Conference
- The Strata + Hadoop World Conferences
- Predictive Analytics World
- MLconf
You can also check local meetup groups.
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