Python Projects & Free Books

To start with Machine Learning:    1. Learn Python    2. Practice using Google Colab     Take these free courses: https://t.me/datasciencefun/290 If you need a bit more time before diving deeper, finish the Kaggle tutorials. At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle. If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed. From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit. The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them: https://t.me/datasciencefree/259 Many different books will help you. The attached image will give you an idea of my favorite ones. Finally, keep these three ideas in mind: 1. Start by working on solved problems so you can find help whenever you get stuck. 2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice. 3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others. During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right. Here is the good news: Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space. Focus on finding your path, and Write. More. Code. That's how you win.✌️✌️

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How to Learn API Development? Learning how to develop APIs is an important skill for modern-day developers. Here’s a mind map of what all you need to learn about API development: 1 - API Fundamentals What is an API, types of API (REST, SOAP, GraphQL, gRPC, etc.), and API vs SDK. 2 - API Request/Response HTTP Methods, Response Codes, and Headers. 3 - Authentication and Security Authentication mechanisms (JWT, OAuth 2, API Keys, Basic Auth) and security strategies. 4 - API Design and Development RESTful API principles include stateless, resource-based URL, versioning, and pagination. Also, API documentation tools like OpenAPI, Postman, Swagger. 5 - API Testing Tools for testing APIs such as Postman, cURL, SoapUI, and so on. 6 - API Deployment and Integration Consuming APIs in different languages like JS, Python, and Java. Also, working with 3rd party APIs like the Google Maps API and the Stripe API. Learn about API Gateways like AWS, Kong, Apigee. Over to you: What else will you add to the list for learning API development?

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10 Must-Know Python Libraries for LLMs in 2025 1. Hugging Face Transformers Best for: Pre-trained LLMs, fine-tuning, inference 2. LangChain Best for: LLM-powered apps, chatbots, AI agents 3. SpaCy Best for: Tokenization, named entity recognition (NER), dependency parsing 4. Natural Language Toolkit (NLTK) Best for: Linguistic analysis, tokenization, POS tagging 5. SentenceTransformers Best for: Semantic search, similarity, clustering 6. FastText Best for: Word embeddings, text classification 7. Gensim Best for: Word2Vec, topic modeling, document embeddings 8. Stanza Best for: Named entity recognition (NER), POS tagging 9. TextBlob Best for: Sentiment analysis, POS tagging, text processing 10. Polyglot Best for: Multi-language NLP, named entity recognition, word embeddings

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Python Patterns

Python Functions 👆👆

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Python Functions 👆

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Type of problem, while solving DSA problem in Array ❗ There are many types of problems that can be solved using arrays and different techniques in Data Structures and Algorithms. Here are some common problem types and techniques that you might encounter: 𝟏. 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐰𝐢𝐧𝐝𝐨𝐰 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are given an array and a window size, and you have to find a subarray of that size that satisfies certain conditions. You can use a sliding window technique to efficiently search through the array by maintaining a current window of fixed size and updating it as you move forward. 𝟐. 𝐓𝐰𝐨 𝐩𝐨𝐢𝐧𝐭𝐞𝐫 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you use two pointers to traverse the array from both ends and find a certain pattern or condition. For example, you can use two pointers to find a pair of elements that sum up to a target value, or to reverse an array. 𝟑. 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to sort an array in a certain way, such as in ascending or descending order, or according to certain criteria such as frequency or value. You can use sorting algorithms such as merge sort or quick sort to efficiently sort the array. 𝟒. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to find a specific element in the array or to search for a certain pattern. You can use searching algorithms such as binary search or linear search to efficiently search through the array. 𝟓. 𝐒𝐮𝐛𝐚𝐫𝐫𝐚𝐲 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to find a contiguous subarray that satisfies certain conditions. You can use techniques such as prefix sum or Kadane's algorithm to efficiently find the subarray with the maximum sum. 𝟔. 𝐂𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to count the occurrences of certain elements or to count the number of subarrays or subsequences that satisfy certain conditions. You can use techniques such as hashing or dynamic programming to efficiently count the occurrences or number of subarrays.

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Python Most Important Interview Questions Question 1: Calculate the average stock price for Company X over the last 6 months. Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data. Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year. Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X. Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question 6: Compare the average stock price of Companies X and Z in the first quarter of the year. #Data# ---------------------------------------------- import pandas as pd data = {   'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'),   'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180),   'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180),   'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) } df = pd.DataFrame(data)

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TOP 10 Python Concepts for Job Interview 1. Reading data from file/table 2. Writing data to file/table 3. Data Types 4. Function 5. Data Preprocessing (numpy/pandas) 6. Data Visualisation (Matplotlib/seaborn/bokeh) 7. Machine Learning (sklearn) 8. Deep Learning (Tensorflow/Keras/PyTorch) 9. Distributed Processing (PySpark) 10. Functional and Object Oriented Programming

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Drawing Beautiful Design Using Python 👇👇

from turtle import *
import turtle as t

def my_turtle():
    # Choices
    sides = str(3)
    loops = str(450)
    pen = 1
    for i in range(int(loops)):
        forward(i * 2/int(sides) + i)
        left(360/int(sides) + .350)
        hideturtle()
        pensize(pen)
        speed(30)

my_turtle()
t.done()

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Python Detailed Roadmap 🚀 📌 1. Basics ◼ Data Types & Variables ◼ Operators & Expressions ◼ Control Flow (if, loops) 📌 2. Functions & Modules ◼ Defining Functions ◼ Lambda Functions ◼ Importing & Creating Modules 📌 3. File Handling ◼ Reading & Writing Files ◼ Working with CSV & JSON 📌 4. Object-Oriented Programming (OOP) ◼ Classes & Objects ◼ Inheritance & Polymorphism ◼ Encapsulation 📌 5. Exception Handling ◼ Try-Except Blocks ◼ Custom Exceptions 📌 6. Advanced Python Concepts ◼ List & Dictionary Comprehensions ◼ Generators & Iterators ◼ Decorators 📌 7. Essential Libraries ◼ NumPy (Arrays & Computations) ◼ Pandas (Data Analysis) ◼ Matplotlib & Seaborn (Visualization) 📌 8. Web Development & APIs ◼ Web Scraping (BeautifulSoup, Scrapy) ◼ API Integration (Requests) ◼ Flask & Django (Backend Development) 📌 9. Automation & Scripting ◼ Automating Tasks with Python ◼ Working with Selenium & PyAutoGUI 📌 10. Data Science & Machine Learning ◼ Data Cleaning & Preprocessing ◼ Scikit-Learn (ML Algorithms) ◼ TensorFlow & PyTorch (Deep Learning) 📌 11. Projects ◼ Build Real-World Applications ◼ Showcase on GitHub 📌 12. ✅ Apply for Jobs ◼ Strengthen Resume & Portfolio ◼ Prepare for Technical Interviews Like for more ❤️💪