Machine learning using spark

1. MACHINE LEARNING USING SPARK

2. The following topics will be covered in our Machine Learning Using Spark  Online Training: Copyright @ 2015 Learntek. All Rights Reserved.

3. What is Machine Learning? • Machine learning Using Spark-Spark MLlib is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. Copyright @ 2015 Learntek. All Rights Reserved.

4. Into to Machine Learning Using Spark • MLlib is  Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. At a high level, it provides tools such as: • ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering • Featurization: feature extraction, transformation, dimensionality reduction, and selection • Pipelines: tools for constructing, evaluating, and tuning ML Pipelines • Persistence: saving and load algorithms, models, and Pipelines • Utilities: linear algebra, statistics, data handling, etc. Copyright @ 2015 Learntek. All Rights Reserved.

5. Tools • This course will be delivered using Scala and PYTHON API. For explaining statistical concept, R language will also be using. Visualization part will be covered using Bokeh/ggplot library. Copyright @ 2015 Learntek. All Rights Reserved.

6. Introduction to Apache Spark • Spark Programming model • RDD and Data Frame • Transformation and Action • Broadcast and Accumulator • Running HDP on local machine • Launching Spark Cluster Copyright @ 2015 Learntek. All Rights Reserved.

7. Basic Statistics  Copyright @ 2015 Learntek. All Rights Reserved.

8. Machine Learning Using Spark • Introduction to Spark MLlib • Data types: Vector, Labeled Point • Feature Extraction • Feature Transformation, Normalization • Feature Selectors • Locality Sensitive Hashing(LSH) Copyright @ 2015 Learntek. All Rights Reserved.

9. Regression Analysis with Spark • Types of Regression Models • Gradient Descent • Linear Regression, Generalized Linear Regression • MSE, RMSE MAE, R-squared Coefficient • Transforming the target variable • Tuning Model Parameters Copyright @ 2015 Learntek. All Rights Reserved.

10. Classification Model with Spark Copyright @ 2015 Learntek. All Rights Reserved.

11. Clustering  • Hierarchical clustering • K-mean clustering Copyright @ 2015 Learntek. All Rights Reserved.

12. Dimensionality Reduction • Principal Component Analysis • Singular Value Decomposition • Clustering as dimensionality reduction • Training a dimensionality reduction model • Evaluating dimensionality reduction models Copyright @ 2015 Learntek. All Rights Reserved.

13. Recommendation Engine • Content based filtering • Collaborative based filtering • Overview of Movie Lens data • Training a recommendation model • Using the recommendation model • Performance Evaluation Copyright @ 2015 Learntek. All Rights Reserved.

14. Text Processing Copyright @ 2015 Learntek. All Rights Reserved.

15. Prerequisites : • Prior  understanding of exploratory data analysis and data visualization  will help immensely in learning machine learning concept and  applications. This  include basic  statistical technique for data analysis. Having some knowledge of R programming or some Python packages like sci-kit, numpy will be useful. However , we are going to cover basic  statistics technique  as part of this course  before going deep into machine learning . This will help everyone to gain maximum from this course. Copyright @ 2015 Learntek. All Rights Reserved.

16. Copyright @ 2015 Learntek. All Rights Reserved.