Machine-Learning-Algorithms-with-Pyspark

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Machine-Learning-Algorithms-with-Pyspark

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In this Complete process in machine learning is discussed and done with pyspark .

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# machine-learning-using-pyspark
#### 1. Understanding PySpark Ecosystem
* Big Data
* Hadoop
* Spark
* PySpark
* Machine Learning using PySpark

#### 2. Foundations of Machine Learning
* Introduction to Machine Learning
* Supervised vs Unsupervised 
* Classification vs Regression
* Data Ingestion
* Data Wrangling
* Data Preprocessing
* Model Training
* Model Validation
* Deployment

#### 3. Internal Details of Spark
* Driver
* Executors 
* Partitions
* Jobs
* Stages 
* Tasks
* Resilient Distributed Datastructure 
* DataFrames as a High Level Datastructure

#### 4. Low level Understanding using RDD
* Creation of RDD
* Transformation methods
* Aggregation methods
* Actions
* Caching
* Debugging

#### 5. Data Ingestion 
* Loading CSV, JSON & parquet
* Connecting to databases
* Getting data from streaming server

#### 5. Data Wrangling using DataFrames
* Descriptive Statistics
* Accessing subsets of data - Rows, Columns, Filters
* Handling Missing Data
* Dropping rows & columns
* Handling Duplicates
* Aggregate functions 
* Merge, Join & Concatenate

#### 6. Data Preprocessing
* Why Preprocessing ?
* Scaling Techniques
* Encoding Techniques
* Text Processing
* Dimensionality Reduction
* Vectorization of Data

#### 7. Regression Learning Models
* Linear Regression
* Decision Tree Regressor
* Random Forest Regressor
* GBT Regressor
* Evaluation of Regression Models

#### 8. Classification Learning Models
* LogisticRegression
* DecisionTreeClassifier
* GBT Classifier
* RandomForestClassifier
* NaiveBayes
* MultiLayerPerceptronClassifier
* Evaluation of Classification Models

#### 9. Clustering Learning Models
* Motivation behind clustering
* KMeans
* GaussianMixtureModel
* Latent Dirichlet Allocation

#### 10. Recommandation Engine

#### 11. Pipeline & Hyper-parameter Tuning
* Composite Estimators using Pipelines
* Model Selection
* Hyper-parameter Tuning
* Persisting trained models
* Deployment

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