Learn Artificial Intelligence and Big Data Step by Step with Examples

These notes are very easy to understand for everyone and if someone has the basic understanding about Java and Hadoop/Spark will be an added advantage. It is for everyone who has curious mind who wants to explore the big data analytics, artificial intelligence and machine learning.

These notes are about artificial intelligence and big data.

Artificial intelligence:  Computer controls the ability of a robot or computer of doing any task or work instead of human.

Big Data: It is the huge amount of unstructured data or complex data from which we extract information and process it through application software.

These notes are helpful for students, programmers, developers and everyone who is interested in artificial intelligence. You can easily download these notes free.

These notes cover following topics:

  1. Big data and artificial intelligence system
    1. What the human brain does best
    2. What the electronic brain does best
    3. Best of both worlds
  2. Ontology for Big Data
    1. Human brain and ontology
    2. Ontology of Information Science
  3. Learning from Big Data
    1. Supervised and unsupervised machine learning
    2. The Spark Programming Model
    3. The Spark MLlib library
    4. Regression analysis
    5. Data clustering
  4. Neural network for big data
    1. Perceptron and linear models
    2. Nonlinearities models
    3. Over fitting
    4. Recurrent neural networks
  5. Deep Big Data Analytics
    1. Building data preparation pipelines
    2. Hyper parameter tuning
    3. Distributed computing
    4. Distributed deep learning
  6. Natural language processing
    1. Natural language processing basic
    2. Feature extract
    3. Apply NLP techniques
    4. Implementing sentiment analysis
  7. Fuzzy systems
    1. Fuzzy logic fundamentals
    2. ANFIS networks
    3. Fuzzy C-means clustering
  8. Genetic Programming
    1. Genetic algorithms structure
    2. KEEL framework
    3. Encog machine learning framework
    4. Introduction to the Weka framework
  9. Swarm intelligence
    1. Swarm intelligence
    2. MASON library
    3. Opt4J library
    4. Application in big data analytics
    5. Handling dynamical data
    6. Multi objective optimization
  10. Reinforcement learning
    1. Reinforcement learning algorithm concepts
    2. Reinforcement learning techniques
    3. SARSA learning
    4. Deep reinforcement learning
  11. Cyber security
    1. Understanding steam processing
    2. Cyber security attack types
    3. Understanding SIEM
    4. Splunk
    5. Arc Sight ESM
  12. Cognitive computing
    1. Cognitive science
    2. Cognitive systems
    3. Application in big data analytics
    4. Cognitive intelligence as a service


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