How do I learn machine learning?

• Start from Series of videos ::https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

•  There were plenty of ways to learn Machine Learning / data mining.
  1) Primary thing is to select and learn most suitable language for data mining. ex: python, R.
  I prefer python.
  
  2)Learning some packages like numpy,pandas,matplotlib,scikit-learn.
  
  3) Just follow any reference book for data mining / Machine Learning.
  
  4) Just learning the algorithm will never make you perfect person in data mining or Machine learning field you have to apply them for some data sets. you can get data sets from internet itself.
  
  5) If you face any problem while applying your algorithm to data sets. Just go to A Programmer's Guide to Data Mining here you can find some very good example of how to apply data mining algorithms to data sets and with some real world  applications. I promise you if your an  really  datamining / Machine learning  aspirant you will enjoy A Programmer's Guide to Data Mining.
  
  6) Once you completed this now you can search for data sets and you can apply your algorithms.
  
  7) You can spent some time on  Coursera. where you will find some coursers belongs to data mining and Machine learning. (you will get videos and problem to practice)  
  
  8) On a final note i can say that quora + google will give you lots of stuff related to data mining.
  
  9) All the best ,have a blast and have fun play with data mining / Machine Learning .
  
  10) You can check this blog once a week.
  dataaspirant

• See this talk by Jeremy Howard : At Kaggle, It’s a Disadvantage To Know Too Much and “Getting In Shape For The Sport Of Data Science”
• Start by practicing on toy datasets in MATLAB and walking through simple examples in Statistics and Neural Network Toolbox
• Below two books are standard introduction texts. They are complementary,   the first one is written from a statistician perspective with lots of data analysis examples and the second one is focusing on algorithms. Both are graduate level texts requiring knowledge of Linear algebra, Probability and statistics and Multivariable calculus. It would also help to take a  class on Optimization but not strictly necessary. The texts are:
• 1)  Hastie, Tibshirani & Friedman, The elements of statistical learning : http://www-stat.stanford.edu/~ti... 
• 2) Bishop, Pattern Recognition and Machine Learning: http://research.microsoft.com/en...

• For a more gentle read you may want to check out the textbook by T.Mitchell
• The most coherent and concise lecture notes are by Andrew Ng,   also see his ML course

• For Natural Language applications see Michael Collins'  NLP course and notes
• For Graphical Models see Daphne Koller's course and book
• For Deep Learning see NN + UFLDL + CNN courses

How do I learn about big data?

I'll try to give a very crude overview of how the pieces fit in together, because the details span multiple books. Please forgive me for some oversimplifications.

• MapReduce is the Google paper that started it all (Page on googleusercontent.com). Its a paradigm for writing distributed code inspired by some elements of functional programming. You don't have to do things this way, but it neatly fits a lot of problems we try to solve in a distributed way. The Google internal implementation is called MapReduce and Hadoop is it's open-source implementation. Amazon's Hadoop instance is called Elastic MapReduce (EMR) and has plugins for multiple languages.
• HDFS is an implementation inspired by the Google File System (GFS) to store files across a bunch of machines when it's too big for one. Hadoop consumes data in HDFS (Hadoop Distributed File System).
• Apache Spark is an emerging platform that has more flexibility than MapReduce but more structure than a basic message passing interface. It relies on the concept of distributed data structures (what it calls RDDs) and operators. See this page for more: The Apache Software Foundation
• Because Spark is a lower level thing that sits on top of a message passing interface, it has higher level libraries to make it more accessible to data scientists. The Machine Learning library built on top of it is called MLib and there's a distributed graph library called GraphX.
• Pregel and it's open source twin Giraph is a way to do graph algorithms on billions of nodes and trillions of edges over a cluster of machines. Notably, the MapReduce model is not well suited to graph processing so Hadoop/MapReduce are avoided in this model, but HDFS/GFS is still used as a data store.
• Zookeeper is a coordination and synchronization service that a distributed set of computer make decisions by consensus, handles failure, etc.
• Flume and Scribe are logging services, Flume is an Apache project and Scribe is an open-source Facebook project. Both aim to make it easy to collect tons of logged data, analyze it, tail it, move it around and store it to a distributed store.
• Google BigTable and it's open source twin HBase were meant to be read-write distributed databases, originally built for the Google Crawler that sit on top of GFS/HDFS and MapReduce/Hadoop. Google Research Publication: BigTable
• Hive and Pig are abstractions on top of Hadoop designed to help analysis of tabular data stored in a distributed file system (think of excel sheets too big to store on one machine). They operate on top of a data warehouse, so the high level idea is to dump data once and analyze it by reading and processing it instead of updating cells and rows and columns individually much. Hive has a language similar to SQL while Pig is inspired by Google's Sawzall - Google Research Publication: Sawzall. You generally don't update a single cell in a table when processing it with Hive or Pig.
• Hive and Pig turned out to be slow because they were built on Hadoop which optimizes for the volume of data moved around, not latency. To get around this, engineers bypassed and went straight to HDFS. They also threw in some memory and caching and this resulted in Google's Dremel (Dremel: Interactive Analysis of Web-Scale Datasets), F1 (F1 - The Fault-Tolerant Distributed RDBMS Supporting Google's Ad Business), Facebook's Presto (Presto | Distributed SQL Query Engine for Big Data), Apache Spark SQL (Page on apache.org ), Cloudera Impala (Cloudera Impala: Real-Time Queries in Apache Hadoop, For Real), Amazon's Redshift, etc. They all have slightly different semantics but are essentially meant to be programmer or analyst friendly abstractions to analyze tabular data stored in distributed data warehouses.
• Mahout (Scalable machine learning and data mining) is a collection of machine learning libraries written in the MapReduce paradigm, specifically for Hadoop. Google has it's own internal version but they haven't published a paper on it as far as I know.
• Oozie is a workflow scheduler. The oversimplified description would be that it's something that puts together a pipeline of the tools described above. For example, you can write an Oozie script that will scrape your production HBase data to a Hive warehouse nightly, then a Mahout script will train with this data. At the same time, you might use pig to pull in the test set into another file and when Mahout is done creating a model you can pass the testing data through the model and get results. You specify the dependency graph of these tasks through Oozie (I may be messing up terminology since I've never used Oozie but have used the Facebook equivalent).
• Lucene is a bunch of search-related and NLP tools but it's core feature is being a search index and retrieval system. It takes data from a store like HBase and indexes it for fast retrieval from a search query. Solr uses Lucene under the hood to provide a convenient REST API for indexing and searching data. ElasticSearch is similar to Solr.
• Sqoop is a command-line interface to back SQL data to a distributed warehouse. It's what you might use to snapshot and copy your database tables to a Hive warehouse every night.
• Hue is a web-based GUI to a subset of the above tools - http://gethue.com 

Lets start from beginning: 

• Introduction to CS Course
  Notes: Introduction to Computer Science Course that provides instructions on coding.
  Online Resources:
  Udacity - intro to CS course,
  Coursera - Computer Science 101
  

• Code in at least one object oriented programming language: C++, Java, or Python
  Beginner Online Resources:
  Coursera - Learn to Program: The Fundamentals,
  MIT Intro to Programming in Java,
  Google's Python Class,
  Coursera - Introduction to Python,
  Python Open Source E-Book
  
  Intermediate Online Resources:
  Udacity's Design of Computer Programs,
  Coursera - Learn to Program: Crafting Quality Code,
  Coursera - Programming Languages,
  Brown University - Introduction to Programming Languages
  
• Learn other Programming Languages
  Notes: Add to your repertoire - Java Script, CSS, HTML, Ruby, PHP, C, Perl, Shell. Lisp, Scheme.
  Online Resources: w3school.com - HTML Tutorial, Learn to code
  
• Test Your Code
  Notes: Learn how to catch bugs, create tests, and break your software
  Online Resources: Udacity - Software Testing Methods, Udacity - Software Debugging
  
• Develop logical reasoning and knowledge of discrete math
  Online Resources:
  MIT Mathematics for Computer Science,
  Coursera - Introduction to Logic,
  Coursera - Linear and Discrete Optimization,
  Coursera - Probabilistic Graphical Models,
  Coursera - Game Theory.
  
• Develop strong understanding of Algorithms and Data Structures
  Notes: Learn about fundamental data types (stack, queues, and bags), sorting algorithms (quicksort, mergesort, heapsort), and data structures (binary search trees, red-black trees, hash tables), Big O.
  Online Resources:
  MIT Introduction to Algorithms,
  Coursera - Introduction to Algorithms Part 1 & Part 2,
  Wikipedia - List of Algorithms,
  Wikipedia - List of Data Structures,
  Book: The Algorithm Design Manual
  
• Develop a strong knowledge of operating systems
  Online Resources: UC Berkeley Computer Science 162
  
• Learn Artificial Intelligence Online Resources:
  Stanford University - Introduction to Robotics, Natural Language Processing, Machine Learning
  
• Learn how to build compilers
  Online Resources: Coursera - Compilers
  
• Learn cryptography
  Online Resources: Coursera - Cryptography, Udacity - Applied Cryptography
  
• Learn Parallel Programming
  Online Resources: Coursera - Heterogeneous Parallel Programming

Tools and technologies for Bigdata:

Apache spark - Apache Spark is an open-source data analytics cluster computing framework originally developed in the AMPLab at UC Berkeley.[1] Spark fits into the Hadoop open-source community, building on top of the Hadoop Distributed File System (HDFS).[2] However, Spark is not tied to the two-stage MapReduce paradigm, and promises performance up to 100 times faster than Hadoop MapReduce for certain applications.

Database pipelining -

                      As you will notice it's just not about processing the data, but involves a lot of other components. Collection, storage, exploration, ML and visualization are critical to the proect's success.


SOLR -  Solr to build a highly scalable data analytics engine to enable customers to engage in lightning fast, real-time knowledge discovery.
        Solr (pronounced "solar") is an open source enterprise search platform from the Apache Lucene project. Its major features include full-text search, hit highlighting, faceted search, dynamic clustering, database integration, and rich document (e.g., Word, PDF) handling. Providing distributed search and index replication, Solr is highly scalable.[1] Solr is the most popular enterprise search engine.[2] Solr 4 adds NoSQL features

S3 - Amazon S3 is an online file storage web service offered by Amazon Web Services. Amazon S3 provides storage through web services interfaces. Wikipedia

Hadoop - Apache Hadoop is an open-source software framework for storage and large-scale processing of data-sets on clusters ofcommodity hardware. Hadoop is an Apache top-level project being built and used by a global community of contributors and users. It is licensed under the Apache License 2.0.  Apache Hadoop

MapReduce : Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.

Corona :

Corona, a new scheduling framework that separates cluster resource management from job coordination.[1] Corona introduces a cluster managerwhose only purpose is to track the nodes in the cluster and the amount of free resources. A dedicated job tracker is created for each job, and can run either in the same process as the client (for small jobs) or as a separate process in the cluster (for large jobs).

One major difference from our previous Hadoop MapReduce implementation is that Corona uses push-based, rather than pull-based, scheduling. After the cluster manager receives resource requests from the job tracker, it pushes the resource grants back to the job tracker. Also, once the job tracker gets resource grants, it creates tasks and then pushes these tasks to the task trackers for running. There is no periodic heartbeat involved in this scheduling, so the scheduling latency is minimized. Ref : Under the Hood: Scheduling MapReduce jobs more efficiently with Corona


HBase : HBase is an open source, non-relational, distributed database modeled after Google's BigTable and written in Java. It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS (Hadoop Distributed Filesystem), providing BigTable-like capabilities for Hadoop. That is, it provides a fault-tolerant way of storing large quantities ofsparse data (small amounts of information caught within a large collection of empty or unimportant data, such as finding the 50 largest items in a group of 2 billion records, or finding the non-zero items representing less than 0.1% of a huge collection).

Zookeeper - Apache ZooKeeper is a software project of the Apache Software Foundation, providing an open source distributed configuration service, synchronization service, and naming registry for large distributed systems.[clarification needed] ZooKeeper was a sub project of Hadoop but is now a top-level project in its own right.

Hive - Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While initially developed by Facebook, Apache Hive is now used and developed by other companies such asNetflix. Amazon maintains a software fork of Apache Hive that is included in Amazon Elastic MapReduce on Amazon Web Services.

Mahout - Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwisescalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification. Many of the implementations use the Apache Hadoop platform. Mahout also provides Java libraries for common maths operations (focused on linear algebra and statistics) and primitive Java collections. Mahout is a work in progress; the number of implemented algorithms has grown quickly,[3] but various algorithms are still missing.

Lucene is a bunch of search-related and NLP tools but it's core feature is being a search index and retrieval system. It takes data from a store like HBase and indexes it for fast retrieval from a search query. Solr uses Lucene under the hood to provide a convenient REST API for indexing and searching data. ElasticSearch is similar to Solr.

Sqoop is a command-line interface to back SQL data to a distributed warehouse. It's what you might use to snapshot and copy your database tables to a Hive warehouse every night.

Hue is a web-based GUI to a subset of the above tools. Hue aggregates the most common Apache Hadoop components into a single interface and targets the user experience. Its main goal is to have the users "just use" Hadoop without worrying about the underlying complexity or using a command line

Pregel and it's open source twin Giraph is a way to do graph algorithms on billions of nodes and trillions of edges over a cluster of machines. Notably, the MapReduce model is not well suited to graph processing so Hadoop/MapReduce are avoided in this model, but HDFS/GFS is still used as a data store.

NLTK - The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python programming language. NLTK includes graphical demonstrations and sample data. It is accompanied by a book that explains the underlying concepts behind the language processing tasks supported by the toolkit, plus a cookbook.

NLTK is intended to support research and teaching in NLP or closely related areas, including empirical linguistics, cognitive science, artificial intelligence, information retrieval, and machine learning.

For Python-
                      Scikit Learn

                      Numpy

                      Scipy

Freebase - Freebase is a large collaborative knowledge base consisting of metadata composed mainly by its community members. It is an online collection of structured data harvested from many sources, including individual 'wiki' contributions.

DBPedia : DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created as part of theWikipedia project. This structured information is then made available on the World Wide Web. DBpedia allows users to query relationships and properties associated with Wikipedia resources, including links to other related datasets. DBpedia has been described by Tim Berners-Lee as one of the more famous parts of the decentralized Linked Data effort.

Visualization tool
                      ggplot in R
                      Tableu
                      Qlikview
                     
Mathematics : )

                      Calculus, Statistic, Probability, linear algebra and coordinate geometry

NER- Named Entity Recognition (NER) labels sequences of words in a text which are the names of things, such as person and company names, or gene and protein names.

Faceted search : Faceted search also called faceted navigation or faceted browsing, is a technique for accessing information organized according to a faceted classification system, allowing users to explore a collection of information by applying multiple filters. A faceted classification system classifies each information element along multiple explicit dimensions, called facets, enabling the classifications to be accessed and ordered in multiple ways rather than in a single, pre-determined, taxonomic order

Source : Wikipedia, the free encyclopedia