Python | Machine Learning | Coding

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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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Python | Machine Learning | Coding | R

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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Python | Machine Learning | Coding | R

Forwarded from Data Science Machine Learning Data Analysis

#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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Python | Machine Learning | Coding | R

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Python | Machine Learning | Coding | R

🔗 Machine Learning from Scratch by Danny Friedman

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

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Python | Machine Learning | Coding | R

Stanford’s Machine Learning - by Andrew Ng

A complete lecture notes of 227 pages. Available Free.

Download the notes:
cs229.stanford.edu/main_notes.pdf

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Python | Machine Learning | Coding | R

"Machine Learning & LLMs for Beginners"

Don't miss these 2 books of 100-pages. Both are #FREE to read.

🌟 The Hundred-Page Machine Learning Book:
themlbook.com/wiki/doku.php

🌟 The Hundred-Page Language Model Book:
thelmbook.com

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Python | Machine Learning | Coding | R

Stanford's "Design and Analysis of Algorithms" Winter 2025

Lecture Notes & Slides: https://stanford-cs161.github.io/winter2025/lectures/

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Python | Machine Learning | Coding | R

"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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Python | Machine Learning | Coding | R

@CodeProgrammer Matplotlib.pdf

4.3 MB

💯

Mastering Matplotlib in 20 Days

The Complete Visual Guide for Data Enthusiasts

Matplotlib is a powerful Python library for data visualization, essential not only for acing job interviews but also for building a solid foundation in analytical thinking and data storytelling.

This step-by-step tutorial guide walks learners through everything from the basics to advanced techniques in Matplotlib. It also includes a curated collection of the most frequently asked Matplotlib-related interview questions, making it an ideal resource for both beginners and experienced professionals.

https://t.me/addlist/0f6vfFbEMdAwODBk

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Python | Machine Learning | Coding | R

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A new interactive sentiment visualization project has been developed, featuring a dynamic smiley face that reflects sentiment analysis results in real time. Using a natural language processing model, the system evaluates input text and adjusts the smiley face expression accordingly:

🙂 Positive sentiment

☹️ Negative sentiment

The visualization offers an intuitive and engaging way to observe sentiment dynamics as they happen.

🔗 GitHub: https://lnkd.in/e_gk3hfe
📰 Article: https://lnkd.in/e_baNJd2

#AI #SentimentAnalysis #DataVisualization #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience

🔗 Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Python | Machine Learning | Coding | R

Python Cheat Sheet

⚡️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Python | Machine Learning | Coding | R

🔥 How to become a data scientist in 2025?

1️⃣

First of all, strengthen your foundation (math and statistics) .

✏️ If you don't know math, you'll run into trouble wherever you go. Every model you build, every analysis you do, there's a world of math behind it. You need to know these things well:

✅ Linear Algebra: Link

✅ Calculus: Link

✅ Statistics and Probability: Link

➖

2️⃣

Then learn programming !

✏️ Without further ado, get started learning Python and SQL.

✅ Python: Link

✅ SQL language: Link

✅ Data Structures and Algorithms: Link

➖

3️⃣

Learn to clean and analyze data!

✏️ Data is always messy, and a data scientist must know how to organize it and extract insights from it.

✅ Data cleansing: Link

✅ Data visualization: Link

➖

4️⃣

Learn machine learning !

✏️ Once you've mastered the basic skills, it's time to enter the world of machine learning. Here's what you need to know:

◀️

Supervised learning: regression, classification

◀️

Unsupervised learning: clustering, dimensionality reduction

◀️

Deep learning: neural networks, CNN, RNN

✅

Stanford University CS229 course: Link

➖

5️⃣

Get to know big data and cloud computing !

✏️ Large companies are looking for people who can work with large volumes of data.

◀️ Big data tools (e.g. Hadoop, Spark, Dask)

◀️ Cloud services (AWS, GCP, Azure)

➖

6️⃣

Do a real project and build a portfolio !

✏️ Everything you've learned so far is worthless without a real project!

◀️ Participate in Kaggle and work with real data.

◀️ Do a project from scratch (from data collection to model deployment)

◀️ Put your code on GitHub.

✅ Open Source Data Science Projects: Link

➖

7️⃣

It's time to learn MLOps and model deployment!

✏️ Many people just build models but don't know how to deploy them. But companies want someone who can put the model into action!

◀️ Machine learning operationalization (monitoring, updating models)

◀️ Model deployment tools: Flask, FastAPI, Docker

✅ Stanford University MLOps Course: Link

➖

8️⃣

Always stay up to date and network!

✏️ Follow research articles on arXiv and Google Scholar.

✅ Papers with Code website: link

✅ AI Research at Google website: link

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Python | Machine Learning | Coding | R

After counting the vehicles, we need to visualize the results on the video frame. We will draw the lane polygons, display the vehicle count for each, and change the lane's color to red if it is considered congested based on our threshold.

# --- Visualization --- (This code continues inside the while loop)

    # Draw the lane polygons on the frame
    cv2.polylines(frame, [LANE_1_POLYGON], isClosed=True, color=(255, 255, 0), thickness=2)
    cv2.polylines(frame, [LANE_2_POLYGON], isClosed=True, color=(255, 255, 0), thickness=2)

    # Check for congestion and display status for Lane 1
    if lane_1_count > CONGESTION_THRESHOLD:
        status_1 = "CONGESTED"
        color_1 = (0, 0, 255) # Red
    else:
        status_1 = "NORMAL"
        color_1 = (0, 255, 0) # Green
    
    cv2.putText(frame, f"Lane 1: {lane_1_count} ({status_1})", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color_1, 2)

    # Check for congestion and display status for Lane 2
    if lane_2_count > CONGESTION_THRESHOLD:
        status_2 = "CONGESTED"
        color_2 = (0, 0, 255) # Red
    else:
        status_2 = "NORMAL"
        color_2 = (0, 255, 0) # Green

    cv2.putText(frame, f"Lane 2: {lane_2_count} ({status_2})", (530, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color_2, 2)
    
    # Display the frame with detections and status
    cv2.imshow("Traffic Congestion Monitor", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

# Hashtags: #DataVisualization #OpenCV #TrafficFlow

---

#Step 5: Results and Discussion

When you run the script, a video window will appear. You will see:
• Yellow polygons outlining the defined lanes.
• Text at the top indicating the number of vehicles in each lane and its status ("NORMAL" or "CONGESTED").
• The status text and its color will change in real-time based on the vehicle count exceeding the CONGESTION_THRESHOLD.

Discussion of Results:
Threshold is Key: The CONGESTION_THRESHOLD is the most important variable to tune. A value of 10 might be too high for a short lane or too low for a long one. It must be calibrated based on the specific camera view and what is considered "congested" for that road.
Polygon Accuracy: The system's accuracy is highly dependent on how well you define the LANE_POLYGON coordinates. They must accurately map to the lanes in the video, accounting for perspective.
Limitations: This method only measures vehicle density (number of cars in an area). It does not measure traffic flow (vehicle speed). A lane could have many cars moving quickly (high density, but not congested) or a few stopped cars (low density, but very congested).
Potential Improvements:
Object Tracking: Implement an object tracker (like DeepSORT or BoT-SORT) to assign a unique ID to each car. This would allow you to calculate the average speed of vehicles within each lane, providing a much more reliable measure of congestion.
Time-Based Analysis: Analyze data over time. A lane that is consistently above the threshold for more than a minute is a stronger indicator of a traffic jam than a brief spike in vehicle count.

#ProjectComplete #AIforCities #Transportation

━━━━━━━━━━━━━━━
By: @CodeProgrammer ✨

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