Machine Learning

Forwarded from Machine Learning with Python

✅ Free ML Books You Should Know!

1⃣ Dive into Deep Learning: https://d2l.ai

🔢 Deep Learning: https://deeplearningbook.org

🔢 Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/

🔢 Machine Learning Yearning: https://info.deeplearning.ai/machine-learning-yearning-book

🔢 Mathematics for Machine Learning: https://mml-book.github.io

🔢 An Introduction to Statistical Learning: https://statlearning.com

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Machine Learning

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🗂

10 “Real Data Science Portfolio” Examples

1️⃣

Ken Jee Portfolio | Data Scientist

▶️

Field: Sports data analysis

👤

Link: Portfolio

2️⃣

Yassine Alouini's Portfolio | Kegel Master

▶️

Domain: Machine Learning and Kegel Competitions

👤

Link: Portfolio

3️⃣

Tatman Portfolio | Data Scientist

▶️

Domain: Natural Language Processing (NLP)

👤

Link: Portfolio

4️⃣

Robinson Portfolio | Data Scientist

▶️

Field: Statistical analysis and R programming

👤

Link: Portfolio

5️⃣

Siraj Raval's Portfolio | AI Instructor

▶️

Field: Machine Learning and Artificial Intelligence

👤

Link: Portfolio

6️⃣

Julia Silge's Portfolio | Data Scientist

▶️

Domain: Organized data and data visualization

👤

Link: Portfolio

7️⃣

Mueller Portfolio | Developer Scikit-Learn

▶️

Field: Machine learning and open source projects

👤

Link: Portfolio

8️⃣

Wickham Portfolio | Data Scientist

▶️

Area: R programming and data visualization

👤

Link: Portfolio

9️⃣

Portfolio of François Puget | Kegel Master

▶️

Domain: Advanced Machine Learning Techniques

👤

Link: Portfolio

🔟

Emily's Portfolio | Data Analyst at Disney

▶️

Domain: Data visualization and storytelling

👤

Link: Portfolio

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Machine Learning

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Machine Learning Glossary

Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.

Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html

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Machine Learning

Forwarded from Machine Learning with Python

1:09:25

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🔥

MIT has updated its famous course 6.S191: Introduction to Deep Learning.

.

All slides, #code and additional materials can be found at the link provided.

📌 Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew

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Machine Learning

Forwarded from Machine Learning with Python

Numpy @CodeProgrammer.pdf

813.2 KB

🏳️‍🌈 "NumPy Library" Tutorial

👨🏻‍💻 For the past few days, I've been busy preparing this comprehensive tutorial on the NumPy library for data science, trying to cover all the tips and tricks of this library.

✅

Why is this booklet different? Because it is not written based on just theoretical concepts, but is the result of my own experiences and learning. It has real and practical examples that will help you better understand #NumPy concepts and use them in your projects.💯

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Machine Learning

👩‍💻 Prompt Engineering: A Practical Example

This real-world project tutorial covers zero-shot and few-shot prompting, delimiters, numbered steps, role prompts, chain-of-thought prompting, and more. Improve your LLM-assisted projects today.

Link: https://realpython.com/practical-prompt-engineering/

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Machine Learning

Forwarded from Machine Learning with Python

🔖 The book that paved the way for me to "data science"!

👨🏻‍💻 "Where do I start now?" This was the first and biggest question I faced when I started my Data Science learning journey!

⏪ I was really overwhelmed by the large number of scattered sources, long courses, and specialized books full of heavy terminology. I didn't know how to start and move forward in this direction...

✔️ But the book Intro to Data Science with Python changed everything for me and gave me a new perspective!

✏️ This book is a complete guide to starting from scratch and is great for both beginners and professionals in this field!! From coding with Python to working with data, visualization, and even AI tools, it explains everything in the simplest and most practical way possible.

💸 A great start for anyone looking to learn data science with Python!👇

┌ 🏳️‍🌈 Intro to Data Science with Python
├ 📄 E-book
└ 🐱 GitHub-Repos

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Machine Learning

Forwarded from Machine Learning with Python

course lecture on building Transformers from first principles:
https://www.dropbox.com/scl/fi/jhfgy8dnnvy5qq385tnms/lectureattentionneuralnetworks.pdf?rlkey=fddnkonsez76mf8bzider3hrv&dl=0

The #PyTorch notebooks also demonstrate how to implement #Transformers from scratch:
https://github.com/xbresson/CS52422025/tree/main/labslecture07

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Machine Learning

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Pandas Introduction to Advanced.pdf

854.8 KB

📄

"Pandas Introduction to Advanced" booklet

👨🏻‍💻 You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need.

✔️ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

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Machine Learning

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Machine Learning

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Exercises in Machine Learning

This book contains 75+ exercises

Download, read, and practice:
arxiv.org/pdf/2206.13446

GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises

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Machine Learning

Forwarded from Machine Learning with Python

Linear Algebra

The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning.

Completely FREE.

Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf

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Machine Learning

#MachineLearning Systems — Principles and Practices of Engineering Artificially Intelligent Systems: https://mlsysbook.ai/

open-source textbook focuses on how to design and implement AI systems effectively

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Machine Learning

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🔗 Machine Learning from Scratch by Danny Friedman

https://dafriedman97.github.io/mlbook/content/introduction.html

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Machine Learning

Forwarded from Machine Learning with Python

"Introduction to Probability for Data Science"

One of the best books on #Probability. Available FREE.

Download the book:
probability4datascience.com/download.html

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Machine Learning

Forwarded from Machine Learning with Python

SciPy.pdf

206.4 KB

Unlock the full power of SciPy with my comprehensive cheat sheet!
Master essential functions for:

Function optimization and solving equations

Linear algebra operations

ODE integration and statistical analysis

Signal processing and spatial data manipulation

Data clustering and distance computation ...and much more!

💯

BEST DATA SCIENCE CHANNELS ON TELEGRAM

🌟

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Machine Learning

Forwarded from Machine Learning with Python

Numpy from basics to advanced.pdf

2.4 MB

📕

Mastering NumPy – From Basics to Advanced

NumPy is an essential library in the world of data science, widely recognized for its efficiency in numerical computations and data manipulation. This powerful tool simplifies complex operations with arrays, offering a faster and cleaner alternative to traditional Python lists and loops.

The "Mastering NumPy" booklet provides a comprehensive walkthrough—from array creation and indexing to mathematical/statistical operations and advanced topics like reshaping and stacking. All concepts are illustrated with clear, beginner-friendly examples, making it ideal for anyone aiming to boost their data handling skills.

🌟

Join the communities:

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Machine Learning

Topic: Python PySpark Data Sheet – Part 1 of 3: Introduction, Setup, and Core Concepts

---

### 1. What is PySpark?

PySpark is the Python API for Apache Spark, a powerful distributed computing engine for big data processing.

PySpark allows you to leverage the full power of Apache Spark using Python, making it easier to:

• Handle massive datasets
• Perform distributed computing
• Run parallel data transformations

---

### 2. PySpark Ecosystem Components

• Spark SQL – Structured data queries with DataFrame and SQL APIs
• Spark Core – Fundamental engine for task scheduling and memory management
• Spark Streaming – Real-time data processing
• MLlib – Machine learning at scale
• GraphX – Graph computation

---

### 3. Why PySpark over Pandas?

| Feature | Pandas | PySpark |
| -------------- | --------------------- | ----------------------- |
| Scale | Single machine | Distributed (Cluster) |
| Speed | Slower for large data | Optimized execution |
| Language | Python | Python on JVM via Py4J |
| Learning Curve | Easier | Medium (Big Data focus) |

---

### 4. PySpark Setup in Local Machine

#### Install PySpark via pip:

pip install pyspark

#### Start PySpark Shell:

pyspark

#### Sample Code to Initialize SparkSession:

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("MyApp") \
    .getOrCreate()

---

### 5. RDD vs DataFrame

| Feature | RDD | DataFrame |
| ------------ | ----------------------- | ------------------------------ |
| Type | Low-level API (objects) | High-level API (structured) |
| Optimization | Manual | Catalyst Optimizer (automatic) |
| Usage | Complex transformations | SQL-like operations |

---

### 6. Creating DataFrames

#### From Python List:

data = [("Alice", 25), ("Bob", 30)]
df = spark.createDataFrame(data, ["Name", "Age"])
df.show()

#### From CSV File:

df = spark.read.csv("file.csv", header=True, inferSchema=True)
df.show()

---

### 7. Inspecting DataFrames

df.printSchema()     # Schema info  
df.columns           # List column names  
df.describe().show() # Summary stats  
df.head(5)           # First 5 rows

---

### 8. Basic Transformations

df.select("Name").show()
df.filter(df["Age"] > 25).show()
df.withColumn("AgePlus10", df["Age"] + 10).show()
df.drop("Age").show()

---

### 9. Working with SQL

df.createOrReplaceTempView("people")
spark.sql("SELECT * FROM people WHERE Age > 25").show()

---

### 10. Writing Data

df.write.csv("output.csv", header=True)
df.write.parquet("output_parquet/")

---

### 11. Summary of Concepts Covered

• Spark architecture & PySpark setup
• Core components of PySpark
• Differences between RDD and DataFrames
• How to create, inspect, and manipulate DataFrames
• SQL support in Spark
• Reading/writing to/from storage

---

### Exercise

1. Load a sample CSV file and display the schema
2. Add a new column with a calculated value
3. Filter the rows based on a condition
4. Save the result as a new CSV or Parquet file

---

#Python #PySpark #BigData #ApacheSpark #DataEngineering #ETL

https://xn--r1a.website/DataScienceM

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