Machine Learning with Apache Spark 3.0 using Scala

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Overview

Lecture 3: What is Spark ML?

Lecture 4: Introduction to Machine Learning

Lecture 5: Tips to Improve Your Course Taking Experience

Chapter 2: Apache Spark Basics (Optional)

Lecture 1: Introduction to Spark

Lecture 2: (Old) Free Account creation in Databricks

Lecture 3: (New) Free Account creation in Databricks

Lecture 4: Provisioning a Spark Cluster

Lecture 5: Basics about notebooks

Lecture 6: Why we should learn Apache Spark?

Lecture 7: Spark RDD (Create and Display Practical)

Lecture 8: Spark Dataframe (Create and Display Practical)

Lecture 9: Anonymus Functions in Scala

Lecture 10: Extra (Optional on Spark DataFrame)

Lecture 11: Extra (Optional on Spark DataFrame) in Details

Lecture 12: Spark Datasets (Create and Display Practical)

Chapter 3: Apache Spark Machine Learning

Lecture 1: Types of Machine Learning

Lecture 2: Steps Involved in Machine Learning Program

Lecture 3: Spark MLlib

Lecture 4: Importing Notebook and Data Upload

Lecture 5: Basic statistics Correlation

Lecture 6: Data Sources

Lecture 7: Data Source CSV File

Lecture 8: Data Source JSON File

Lecture 9: Data Source LIBSVM File

Lecture 10: Data Source Image File

Lecture 11: Data Source Arvo File

Lecture 12: Data Source Parquet File

Lecture 13: Machine Learning Data Pipeline Overview

Lecture 14: Machine Learning Project as an Example (Just for Basic Idea)

Lecture 15: Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 1

Lecture 16: Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 2

Lecture 17: Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia) 3

Lecture 18: Components of a Machine Learning Pipeline

Lecture 19: Extracting, transforming and selecting features

Lecture 20: TF-IDF (Feature Extractor)

Lecture 21: Word2Vec (Feature Extractor)

Lecture 22: CountVectorizer (Feature Extractor)

Lecture 23: FeatureHasher (Feature Extractor)

Lecture 24: Tokenizer (Feature Transformers)

Lecture 25: StopWordsRemover (Feature Transformers)

Lecture 26: n-gram (Feature Transformers)

Lecture 27: Binarizer (Feature Transformers)

Lecture 28: PCA (Feature Transformers)

Lecture 29: Polynomial Expansion (Feature Transformers)

Lecture 30: Discrete Cosine Transform (DCT) (Feature Transformers)

Lecture 31: StringIndexer (Feature Transformers)

Lecture 32: IndexToString (Feature Transformers)

Lecture 33: OneHotEncoder (Feature Transformers)

Lecture 34: SQLTransformer (Feature Transformers)

Lecture 35: VectorAssembler (Feature Transformers)

Lecture 36: RFormula (Feature Selector)

Lecture 37: ChiSqSelector (Feature Selector)

Lecture 38: Classification Model

Lecture 39: Decision tree classifier Project

Lecture 40: Logistic regression Model (Classification Model It has regression in the name)

Lecture 41: Naive Bayes Project (Iris flower class prediction)

Lecture 42: Random Forest Classifier Project

Lecture 43: Gradient-boosted tree classifier Project

Lecture 44: Linear Support Vector Machine Project

Lecture 45: One-vs-Rest classifier (a.k.a. One-vs-All) Project

Lecture 46: Regression Model

Lecture 47: Linear Regression Model Project

Lecture 48: Decision tree regression Model Project

Lecture 49: Random forest regression Model Project

Lecture 50: Gradient-boosted tree regression Model Project

Lecture 51: Clustering KMeans Project (Mall Customer Segmentation)

Lecture 52: Explanation of few terms used in Model

Lecture 53: Linear Regression Model Project – Predict Ads Click

Chapter 4: Download Resources

Lecture 1: Download Resources

Lecture 2: Important Lecture

Lecture 3: Bonus Lecture