Free PySpark, Python & SQL Tutorials
Every tutorial on PySpark.in, grouped by technology. All are free, and the code examples run in the browser-based compiler with no setup.
Azure
Basic Mathematics
Deep Learning
Digital Image Processing
Linear Algebra
- Introduction to Linear Algebra
- Linear Equations
- Understanding Vector Spaces
- Matrices
- Matrix Multiplication
- ThreeEquationsThreeUnknowns
- Vectors& Linear Equations
- Dot Product
- Independence Basis Dimension
- The Complete Solution to Ax = b
- The Nullspace of A: Solving Ax = 0
- Spaces of Vectors
Machine Learning
- Introduction to Machine Learning
- Introduction to Deep Learning
- A Beginner's Guide to Machine Learning
- The First Artificial Neuron and Learning from Mistakes
- Patterns
- Understanding Vectors, the Building Blocks of AI
- How Vectors, Dot Products, and Hyperplanes Power the Perceptron
- Guaranteed to Succeed — How the Perceptron Promises to Find the Answer
- The Beautiful Math Behind Learning — Understanding the Perceptron's Update Rule
- The Proof That Machines Always Learn — Understanding Why the Perceptron Never Gives Up
- Gradient Descent
- Probability — The Secret Language of Machine Learning
- The Simplest Way Machines Learn — Finding Friends in a Crowd
- When Too Many Directions Confuse the Machine — The Curse of Dimensionality
- Finding the Secret Directions in Your Data — PCA, Eigenvalues, and Eigenvectors
- The Magic Trick That Changed Machine Learning Forever Introduction: A Story from Bell Labs
- One Hidden Layer Is All You Need? The Surprising Story of George Cybenko and the Universal Power of Neural Net
- The Algorithm That Ended the "Neural Networks Are Dead" Myth
- The Magic Trick That Made Deep Learning Possible: Understanding Backpropagation
Nlp
- Natural Language Understanding (NLU) , Natural Language Generation (NLG) and phases of NLP
- Tokenization in NLP and NLP Project Life Cycle
- Coverting The Text to Vector(one hot encoding and bag of words method)
- Convert text to vector: N-grams and TF-IDF method
- Word Embedding
- What is Natural Language Processing ?
- Working with Text in NLP
- Breaking Text into Meaning units
Nlp
- Course Overview
- Accessing Text Corpora and Lexical Resources
- Words and Tokens
- N-gram Language Models
- Embeddings
- Lexical Semantics
- Distributed Word Representations
- Naive Bayes for Text Classification
- Support Vector Machine (SVM)
- HMM and CRF for Sequence Labelling in NLP
- Decision Trees for NLP
- Logistic Regression for Text Classification in NLP
- Bag of Words (BoW) in NLP
- Word2Vec in NLP
- GloVe and FastText in NLP
- Contextual Embeddings in NLP (ELMo, BERT, GPT)
- Sentence Embeddings in NLP (SBERT & USE)
- Subword Models in NLP (BPE & WordPiece)
- Sparse Embeddings in NLP (BM25 & SPLADE)
Python
- What is Python and Why is it used for Data Science and Data Engineering?
- How Does Python Work in the Backend? Internal Working of Python
- Top 30 Python Interview Questions for Data Science
- 3-Month Python Roadmap to Excel in Data Science and Machine Learning
- Python Data Types Explained – A Beginner’s Guide
- Introduction To Python
- Encapsulation in Python
- Variable and Data Types
- Operators in Python
- Install Python on Windows
- Install Python on Linux
- Install Python on macOS
- Conditional Statements (Decision Making)
- Jump Statements
- Looping Statements (Iteration)
- Lists in Python
- Tuples in Python
- Dictionary in Python
- Sets in Python
- Maps in Python
- Strings Introduction
- String Indexing and Slicing
- String Operations
- Arrays in Python
- Exception Handling in Python
- Function and Pass Statement
- Beyond the Basics of Function
- Introduction to Object-Oriented Programming
- Python Classes and Objects
- Python __init__() Method and self Parameter
- Python Class Properties and Methods
- Python Inheritance
- Polymorphism Python
- File I/O and Modules in Python
- Introduction to Modules and Types
- NumPy
- Pandas
- Advance Pandas
- Part 1-Matplotlib Basics & Essential Plots
- PART 2 — Intermediate Concepts & Layouts
- PART 3 — Advanced Visualization & 3D Plots
- Built-In functions and Methods
- Exception Hierarchy in Python
- BASICS OF FUNCTIONS
- Function Arguments
- Return Statements
- Introduction to Pandas
- Data Inspection & Exploration
- Data Selection & Indexing
- Data Cleaning & Data Preprocessing
- Data Manupulation
- Grouping & Aggregation
- Merging & Combining Data
- Time Series Data
- Advance Pandas
- Visualization & EDA (Exploratory Data Analysis)
- Introduction to NumPy
- NumPy ndarray & Array Creation Methods
- Indexing & Slicing in NumPy
- Data Types in NumPy
- Variable and Data Types
- Patterns
- Regex (Regular Expressions)
- Password Detect
- Sales Data Analysis
Software Engineering
Spark
- Apache Spark Runtime Architecture
- Introduction to RDD
- Actions vs Transformations
- Lazy Evaluation in PySpark
- DataFrame Operations in PySpark
- Spark SQL Basics
- Running SQL Queries on DataFrames
- Registering UDFs in SQL
- PySpark Joins
- Broadcast and Skew Joins
- Built-in functions
- Aggregate & Array Functions
- String functions
- Date functions
- User-Defined Functions (UDFs)
- PySpark Aggregations
- PySpark Aggregations
- PySpark Aggregations
- Rollup Cube and Pivot
- Handling Null Values
- Date and Timestamp Functions
- Date and Timestamp Functions
- Reading CSV, JSON, and Parquet Files
- Performance Optimization
- PySpark with AWS
- Pitfalls of Data Lakes
- Lakehouse Architecture
- PySpark Window Functions (Beginner Friendly Guide)
- What is PySpark
- ETL, ELT and ETLT.
- ETL PIPELINE FOR DATA ENGINEERS
- Log Analysis Dashboard
- ETL Architecture
- Orchestration Tools
- The Genesis of Spark
- Spark Revolution
- Hadoop to PySpark
- Install PySpark on Windows
- Spark Architecture & Execution model
- Introduction to Pyspark DataFrames
- DataFrame Creation in Pyspark
- Exploring DataFrames
- Understanding Schema in PySpark
- Rows & Columns in PySpark
- Pyspark Data Types
- Partition Basics
- Dispalying DataFrames
- Understanding Apache Spark RDDs
- Data Engineering Foundations and Core Concepts
- Data Engineering: Essential Concepts Explained
Sql
- Introduction to Databases
- Natural Join
- INNER JOIN
- LEFT JOIN
- RIGHT JOIN
- FULL JOIN
- CROSS JOIN
- SELF JOIN
- Joins on Multiple Tables
- Introduction to Databases
- Creating Tables
- Inserting Rows
- Retrieving Data
- Update Rows
- Delete Rows
- Alter Tables
- Alter Tables
- Comparison Operators
- String Operations
- Logical Operators
- IN and BETWEEN Operators
- ORDER BY and DISTINCT
- Aggregations
- Group By
- SQL Wildcards
- SQL Expressions
- SQL Functions
- Core Concepts of ER Model
- Creating a Relational Database
- Window Functions
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