- Fri, Mar 23, 2018 10:00 AM
25 Francis St
Latitude: -31.9488, Longitude: 115.861
About the Venue: State Library of Western Australia, Alexander Library Building, Perth Cultural Centre Day 1: Great Southern Room Day 2: Kimberley Room For more Information visit: http://www.datasmelly.com/trainingAppliedML.html Outline This course provides you with a complete introduction to Machine Learning. It is one of the most comprehensive two days course in Machine Learning. The goal of this offering is to bring "Machine Learning methods and techniques" out of research Labs and share its ownership with other parties who can greatly benefit from it: •We believe that a lot of gap exists between research world (academia) and industry. A goal of this course is to close that gap. •Secondly, we believe that a careful designed machine learning component can greatly differentiate an application/product from its competitors. This, however, requires under-the-hood understanding of machine learning. The goal of this course is to provide an excellent foundation in machine learning, so that one can think in terms of machine learning concepts and can easily incorporate analytical algorithms in their applications. The course is combination of both theory and practice. It not only provides a good overview of main machine learning concepts but also provide guidelines to apply these concepts to solve domain specific real world problem. The course is update-to-date with the latest research. For example, it covers recent topics in Machine Learning such as Factorization Machines (very popular in online advertisement placement, large-scale learning, etc.), Feature Engineering (secret sauce behind all practical and effective algorithms), Deep Learning, etc. Other main topics include, fundamental problems such as classification, regression, prediction, anomaly detection, model selection, clustering, dimensionality reduction, recommender systems, etc. Duration 2 days (8 + 8 hours) Training Breakdown (*) Note the unit breakdown in the following is likely to change, but it provides a general overview of the topics that this training will cover. Module Session 1 -- Introduction 1A - Machine learning, Artificial Intelligence, Statistics, Data Mining and More 1B- Machine learning applications in our daily lives 1C - Introduction to Data Science and Big Data 1D - Ingredients of Machine Learning -- Data, Model and Process Statistics 101 Basic Elements of Statistics Random Variables, Probability Density/Mass Function, Expectations Rules of Probability 1E - Training your first practical model Session 2 -- Data Wrangling Lab 2A - Python 101 2B - Introduction to Data Structures in Python 2C - Storing and Manipulating Data Session 3 -- Supervised Machine Learning 3A - Regression Linear Regression, Polynomial Regression 3B - Classification Logistic Regression Generative vs. Discriminative Learning LDA/QDA Naive Bayes, Decision Trees Nearest Neighbour Methods 3C - Prediction Moving Averages ARIMA 3D - Rank Learning 3E - Structure Learning Hidden Markov Models Conditional Random Fields Session 4 -- Machine Learning Lab I 4A - Introduction to Sci-kit Library 4B - Classification/Regression/Prediction/Ranking examples Session 5 -- Model Selection 5A - Bias and Variance Analysis 5B - Achieving Low-variance Regularization Feature Selection 5C - Achieving Low-bias Feature Construction Kernel and Kernel trick 5D - Feature Engineering Generalized Linear Models Factorization Machines Deep Learning 5E - Evaluating and Comparing Models Cross-validation Lift Charts, ROC, RPC, other metrics Statistical Tests, Null-Hypothesis, Friedman Statistics, etc. Session 6 -- Un-Supervised Machine Learning 6A - Clustering K-means, DB-Scan, Hierarchical 6B - Density Estimation 6C - Bayesian Networks 6D - EM Algorithm for Clustering and Gaussian Mixture Models 6E - Curse of Dimensionality 6F - Similarity Measurements Exact vs. Approximate Similarity 6G - Local Sensitive Hashing (LSH) 6H - Data Pre-processing Data Standardization Data Munging Feature Hashing 6I - Dimensionality Reduction Eigen-Value Decomposition Principal Component Analysis (PCA) 6J - Overview of Anomaly Detection 6K - Association Rules and Discovery Apriori Algorithm Session 7 -- Machine Learning Lab II 7A - Building a Machine Learning evaluation framework 7B - Clustering and visualizing Examples Session 8 -- Recommender Systems 8A - Data Structure of Recommender Systems 8B - Content-based Recommendations Collaborative Filtering Memory-based Model-based Others 8C - Addressing cold-start problems 8D - Content-based Recommendations 8E - Collaborative Filtering Revisited Matrix Factorization (SVD and others) 8F - Advertising on the Web Ad Placement Session 9 -- Advanced Machine Learning 9A - Ensemble Learners Boosting, Bagging, Stacking Random Forests and Gradient Boosting 9B - Deep Learning Artificial Neural Networks Auto-Encoders and Boltzmann Machines Deep Belief Networks Convolutional Neural Networks Recurrent Neural Networks 9C - Text Mining Name Entity Recognition Topic Models 9D - A/B/n Testing Randomization Latest trends in AB Testing from software engineering perspective 9E - Stream Mining 9F - Large Scale Machine Learning Session 10 -- Machine Learning Lab III 10A - Netflix Challenge 10B - Ensembling examples 19C - Deep Learning with Tensor Flow Session 11 -- Delegate Presentations 11A - Each delegate (or group of delegates) will have 5 minutes to present a Data Science Problem 11B - Devise a solution based on concepts taught in this training 11C - Feedback from the audience Session 12 -- Networking Salient Features Comprehensive and State-of-the-art training in Data Analytics Small Group -- Max 15. Opportunity to meet and mingle Opportunity for a lot of group discussions and a chance to talk about your own work for 5 minutes in front of the group Frequent Questions Why should I attend this course? Good question! Let us ask you some counter questions. 1) Are you interested in exploring Machine Learning with some breath and depth? 2) Are you curious about the inner workings of most analytic algorithms? 3) Do you want to understand how machines learn from data? 4) Trying to figure out the latest trends in Analytics? 5) Interested in building a superior Machine Learning algorithm for your product or application? 6) Want a through exposition to Machine learning, but too busy to read all the books, research papers and blogs? If answer to any of the above questions is yes, then you should attend this course. How is this course different from others? There are not many courses on Machine Learning, most are offered as part of post-graduate degree or diploma by universities. Non-academic units have a too narrow focus on certain technologies, for example, 'Machine Learning with R' or 'Machine Learning with Microsoft Technologies'. The extra layer of technology around the algorithms confuses the underlying message. We have designed this course around the core concepts conveyed in a total technology agnostic way. The underlying concepts are taught through mathematical notation. Do I need a deep Math background? No, elementary (or high school) level Maths is desirable, but not necessary. Module 1 covers the background in Statistics and Linear Algebra and is sufficient for grasping later concepts in the course. Do I need to bring my laptop? Yes, there are four Laboratories where practical elements of Machine Learning will be illustrated. It will be beneficial if you bring your laptop, to do exercises on your computer. What does course-kit consist of? 1) Book consisting of printed slides (over 300 pages) 2) USB stick consisting of code used in labs 3) Certificate of Attendance (posted in 2-3 weeks after attendance) 4) Welcome pack Who will deliver this unit? This course will delivered by a 'Principle Data Scientist' (highest designation at DataSmelly) -- the coordinator will have a Ph.D in Machine Learning or related area and over six years of research and development experience.