Hi all... I have my new publication on large scale machine learning with Packt publishers coming up in the next few months. Request help from experts in machine learning for help. Please inbox me interest to [email protected] and
A quick note on the scope of content covered in the book:
Practical Machine learning - to be published by Packt
This book focuses on exploring all the Machine Learning techniques and some specific behavioral differences or implementation intricacies with the parallel or distributed processing approach. Additionally, for each technique along with a deep dive on internals of each algorithm, example implementations using top and evolving machine learning frameworks and tools like R, SPSS, Apache Mahout, Python, Julia and Spark is explained. This book helps readers master Machine Learning techniques and gain ability to identify and apply appropriate techniques in the given problem context. In the context of large datasets, multi-core cluster based learning, distributed learning, parallel computation tools and libraries and more. The readers will be exposed to a list of machine learning frameworks and for each of the frameworks detailed implementation aspects like function libraries, syntax, installation or set-up and integration with Hadoop (wherever applicable) will be covered.
- Introduction to Machine learning: Categories, Techniques and Architecture.
- Understand the basic differences between Data Mining, Machine learning and Artificial Intelligence.
- Understand key characteristics, common pitfalls and learn techniques to prepare “large” data sets for machine learning.
- Explore the parallel and distributed environments and approaches for storing and processing large datasets in batch and real-time.
- Introduction to Hadoop, MapReduce along with its latest and relevant eco-system frameworks. Understand the suitability or relevance of Hadoop for Machine Learning and with large datasets.
- Set-up your own Hadoop environment in preparation for an exciting journey to build your own machine learning procedures using different Hadoop compatible machine learning libraries or frameworks.
- Explore Mahout, R, Julia, Python and Spark as options for Machine Learning Tools & Frameworks.
- For each of the framework, learn the syntax and get an understanding of the libraries and usage.
- Set-up the tool and learn the implementation for each of the Machine Learning Technique.
- Learn different machine learning algorithms along with the usage context. Machine Learning algorithms covered will be:
- Supervised Learning: Classification Techniques: Decision Trees, Statistical Decision Trees, Random Forests, Bayesian Classification, SVMs, Nearest Neighbors, Regression Techniques: Linear and non-linear regression, multiple regression, Logistic regression
- Unsupervised Learning: Clustering Techniques: Distance Measure, Hierarchical Clustering, Partitional Clustering (K-Means, Guassian). Fuzzy c-means, Kernel based and Graph based, Neural Networks, Association Rule learning.
- Reinforcement Learning: Temporal difference learning, Q-learning and Monte Carlo
- Deep Learning: Deep neural networks, deep belief networks and more.
- Accuracy and Error Measures
- Ensemble Methods: Bagging & Boosting
- Choosing the right Machine Learning Technique and algorithm given the problem context
- Compare and contrast the explored machine learning libraries and frameworks and be aware of other options in the market.
To ensure readers understand the nuances of implementing specific frameworks and appreciate the strengths and concerns of each alternative, this book uses a common real-world problem statement. Necessary care is taken to include commonly used and upcoming machine learning frameworks are included.