Machine Learning and Big Data

Machine Learning and Big Data

1. Applications of ML with Big Data

2. Classification of ML algorithms

   • Learning Methods
     • *Supervised Learning *Unsupervised Learning *Reinforcement Learning
   • Tasts to perfom
     • * Classification *Regression *Clustering *Dimensionality Reduction

3. Platforms and Software for ML

   • *Spark MLib *Azure ML *Mahout *H20 *Spehere *others

4. Challenges

   • Proposed solutions/approaches

5. Platforms and software for B.D.

   • *Hadoop *Pentaho *Others

6. Processing Systems of Big Data

   • *Batch Processing *Stream Processing *Interactive Analysis

7. Analytical Methods of Big Data

   • *Descriptive *Predictive *Prescriptive

8. Characteristics of Big Data

   • 5V's *Volume *Velocity *Variety *Veracity *Value