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Introduction
Introduction
Overview
What is Spark ML
Introduction to Machine Learning
Setting Up the Environment
Impact of Databricks Community Edition Changes (2026) & Transition to Zeppelin
Requirements
(Hands On) Installing JAVA
Steps for Installing JAVA
(Hands On) Setting JAVA environments
Steps for Setting JAVA environments
(Hands On) Apache Zeppelin Installation Steps on Ubuntu machine
Steps for Installing Apache Zeppelin on Ubuntu machine
(Hands On) Installing Docker Desktop on Windows 10/11
Steps for Installing Docker on Windows
(Hands On) Running Apache Zeppelin on Docker (Windows)
Steps for Running Apache Zeppelin on Docker
(Hands On) Configure and Connect to Spark interpreter
Steps for Configure and Connect to Spark Interpreter
Zeppelin Basics
What is Zeppelin
Features & Benefits
Notebook UI Overview
Markdown and text formatting
Creating and running paragraphs
Hands on Creating and Running paragraphs
Visualization Options (Tables, Bar chart, Pie chart, etc.)
Hands On - Types of Default Chart in Zeppelin
Zeppelin with Apache Spark
Spark interpreter details
Working with RDDs and DataFrames
Spark SQL queries and caching
Visualizing Spark outputs
Job tracking and performance tuning basics
Apache Spark Basics (Optional)
Introduction to Apache Spark
(Old) Free Account creation in Databricks
Login (New) Free Account creation in Databricks
Provisioning a Spark Cluster
Basics about notebooks
Why we should learn Apache Spark?
Spark RDD (Create and Display Practical)
Spark Dataframe (Create and Display Practical)
Anonymus Functions in Scala
Extra (Optional on Spark DataFrame)
Extra (Optional on Spark DataFrame) in Details
Spark Datasets (Create and Display Practical)
Apache Spark Machine Learning
Types of Machine Learning
Steps involved in Machine Learning Program
Spark MLlib
Importing Notebook and Data Upload
Basic statistics Correlation
Data Source
Data Source CSV File
Data Source JSON File
Data Source LIBSVM File
Data Source Image File
Data Source Arvo File
Data Source Parquet File
Machine Learning Data Pipeline Overview
Machine Learning Project as an Example
Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 1
Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 2
Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 3
Components of a Machine Learning Pipeline
Extracting, transforming and selecting features
TF-IDF (Feature Extractor)
Word2Vec (Feature Extractor)
CountVectorizer (Feature Extractor)
FeatureHasher (Feature Extractor)
Tokenizer (Feature Transformers)
StopWordsRemover (Feature Transformers)
n-gram (Feature Transformers)
Binarizer (Feature Transformers)
PCA (Feature Transformers)
Polynomial Expansion (Feature Transformers)
Discrete Cosine Transform (DCT) (Feature Transformers)
StringIndexer (Feature Transformers)
IndexToString (Feature Transformers)
OneHotEncoder (Feature Transformers)
SQLTransformer (Feature Transformers)
VectorAssembler (Feature Transformers)
RFormula (Feature Selector)
ChiSqSelector (Feature Selector)
Classification Model
Decision tree classifier Project
Logistic regression Model (Classification Model It has regression in the name)
Naive Bayes Project (Iris flower class prediction)
Random Forest Classifier Project
Gradient-boosted tree classifier Project
Linear Support Vector Machine Project
One-vs-Rest classifier (a.k.a. One-vs-All) Project
Regression model
Linear Regression Model Project
Decision tree regression Model Project
Random forest regression Model Project
Gradient-boosted tree regression Model Project
Clustering KMeans Project (Mall Customer Segmentation)
Explanation of few terms used in Model
Linear Regression Model Project - Predict Ads Click
Download Data
Download Source Code
Predict Ads Code and Data (Project)