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In this course, you will :
- Learn and master the art of Machine Learning through hands-on projects, and then execute them up to run on Databricks cloud computing services (Free Service) in this course.
Syllabus :
1. Introduction
- Overview
- What is Spark ML?
- Introduction to Machine Learning
2. Apache Spark Basics
- Introduction to Spark
- 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)
3. Apache Spark Machine Learning
- Types of Machine Learning
- Steps Involved in Machine Learning Program
- Spark MLlib
- Importing Notebook and Data Upload
- Basic statistics Correlation Preview
- Data Sources
- Data Source CSV File Preview
- Data Source JSON File
- Data Source LIBSVM File
- Data Source Image File Preview
- Data Source Arvo File
- Data Source Parquet File
- Machine Learning Data Pipeline Overview
- Machine Learning Project as an Example (Just for Basic Idea)
- Machine Learning Pipeline Example Project (Will it Rain Tomorrow in Australia)
- 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
- Download Resources
