Machine Learning

Machine Learning Using Spark

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. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. Copyright @ 2019 Learntek. All Rights Reserved.

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 @ 2019 Learntek. All Rights Reserved.

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. 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 @ 2019 Learntek. All Rights Reserved.

Basic Statistics Descriptive Statistics • Mean, Mode, Media, Range, Variance, Standard Deviation, Quartiles, Percentiles Sampling Sampling Methods Sampling Errors Probability Distributions• Normal distribution, t-distribution, Chi-square, F Margin of Error, Confidence Interval, Significance level, Degree of Freedom Hypothesis concept, Type I and Type II error P-value, t-Test, Chi-square Test Correlation Coefficient Copyright @ 2019 Learntek. All Rights Reserved.

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 @ 2019 Learntek. All Rights Reserved.

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 @ 2019 Learntek. All Rights Reserved.

Classification Model with Spark Types of Classification Models • Linear Models, Naives Bayes Model, Decision Tree Logistic Regression Linear Support Vector Machine Random Forest Gradient-Boosted Trees Training Classification Models Accuracy and prediction error Precision and Recall ROC curve and AUC Cross validation Copyright @ 2019 Learntek. All Rights Reserved.

Clustering Hierarchical clustering K-mean clustering Dimensionality Reduction Principal Component Analysis Singular Value Decomposition Clustering as dimensionality reduction Training a dimensionality reduction model Evaluating dimensionality reduction models Copyright @ 2019 Learntek. All Rights Reserved.

Recommendation Engine Content based filtering Collaborative based filtering Overview of Movie Lens data Training a recommendation model Using the recommendation model Performance Evaluation Text Processing Feature Hashing TF-IDF model Tokenization Stop words TF-IDF Weightings Training a TF-IDF model Usage of TF-IDF model Evaluating TF-IDF models Copyright @ 2019 Learntek. All Rights Reserved.

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 @ 2019 Learntek. All Rights Reserved.

Machine Learning