Coding Free Books | Python | AI

Coding is just like the language we use to talk to computers. It's not the skill itself, but rather how do I innovate? How do I build something interesting for my end users?

In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.

Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.

Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.

Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.

Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.

I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.

❤7

1.21K views10:56

Coding Free Books | Python | AI

Important questions to ace your machine learning interview with an approach to answer:

1. Machine Learning Project Lifecycle:
   - Define the problem
   - Gather and preprocess data
   - Choose a model and train it
   - Evaluate model performance
   - Tune and optimize the model
   - Deploy and maintain the model

2. Supervised vs Unsupervised Learning:
   - Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
   - Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).

3. Evaluation Metrics for Regression:
   - Mean Absolute Error (MAE)
   - Mean Squared Error (MSE)
   - Root Mean Squared Error (RMSE)
   - R-squared (coefficient of determination)

4. Overfitting and Prevention:
   - Overfitting: Model learns the noise instead of the underlying pattern.
   - Prevention: Use simpler models, cross-validation, regularization.

5. Bias-Variance Tradeoff:
   - Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.

6. Cross-Validation:
   - Technique to assess model performance by splitting data into multiple subsets for training and validation.

7. Feature Selection Techniques:
   - Filter methods (e.g., correlation analysis)
   - Wrapper methods (e.g., recursive feature elimination)
   - Embedded methods (e.g., Lasso regularization)

8. Assumptions of Linear Regression:
   - Linearity
   - Independence of errors
   - Homoscedasticity (constant variance)
   - No multicollinearity

9. Regularization in Linear Models:
   - Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.

10. Classification vs Regression:
    - Classification: Predicts a categorical outcome (e.g., class labels).
    - Regression: Predicts a continuous numerical outcome (e.g., house price).

11. Dimensionality Reduction Algorithms:
    - Principal Component Analysis (PCA)
    - t-Distributed Stochastic Neighbor Embedding (t-SNE)

12. Decision Tree:
    - Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.

13. Ensemble Methods:
    - Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).

14. Handling Missing or Corrupted Data:
    - Imputation (e.g., mean substitution)
    - Removing rows or columns with missing data
    - Using algorithms robust to missing values

15. Kernels in Support Vector Machines (SVM):
    - Linear kernel
    - Polynomial kernel
    - Radial Basis Function (RBF) kernel

Data Science Interview Resources
👇👇
https://topmate.io/coding/914624

Like for more 😄

❤2🔥1

1.15K views11:17

Coding Free Books | Python | AI

Vuejs complete guide.pdf

6.1 MB

🧿 The Ultimate Vue.js Guide Pdf ✅

One of the most frameworks used for frontend 🔰

React ❤️ and like my posts above 😍

❤2

1.2K views12:45

Coding Free Books | Python | AI

❤3👍1

1.5K views15:24

Coding Free Books | Python | AI

React Js.pdf

34.1 MB

React js full pdf 😍🚀

React with "❤️" For more FREE PDFS

javascript-interview-questions-answers.pdf

212.7 KB

Javascript Interviews Q A For a job 📝

JavaScript Notes 🔥.pdf

484.8 KB

Full notes Javascript From Basic-to-Advance 😍

top-50-html-interview-questions.pdf

98.6 KB

HTML Q A Top 50 📝🤩

keyboard-shortcuts-windows.pdf

142.3 KB

Vscode CheatSheet Windows💥👆

React if you would like CheatSheets, internships, Free notes and more 🤩

❤8

1.95K views18:47

Coding Free Books | Python | AI

The-Quick-Python-Book.pdf

3.2 MB

Quick Python Book ✅

Python Complete Tutorial .pdf

2.3 MB

ML Cheatsheet 🔥🔥😎 (2).pdf

6.2 MB

Locators in Selenium.pdf

957.9 KB

Git Cheat Sheet (1).pdf

641.1 KB

❤8

1.57K views17:55

Coding Free Books | Python | AI

❤4

1.31K views09:36

Coding Free Books | Python | AI

10 Python Mini Projects for Beginners

Guys, once you've got the basics of Python down, it’s time to build stuff!

Here are 10 mini project ideas that are fun, practical, and boost your confidence!

1. Number Guessing Game 🎯
The computer picks a number, and the user keeps guessing until they get it right.
Perfect to practice loops, conditionals, and user input.

2. Calculator App ➕➖✖️➗
Build a simple calculator that takes two numbers and performs addition, subtraction, multiplication, or division.

3. To-Do List (Console Version) ✅
Let users add, view, and delete tasks. Great to practice lists and file handling if you want to save tasks.

4. Password Generator 🔐
Create random passwords using letters, numbers, and symbols. Use the random and string modules.

5. Dice Rolling Simulator 🎲
Simulate rolling a die. Add cool features like rolling multiple dice or counting the frequency.

6. Rock Paper Scissors Game ✊✋✌️
Let the user play against the computer. Introduces randomness and conditional logic.

7. Quiz App ❓
Create a multiple-choice quiz that gives a score at the end. Store questions and answers using dictionaries.

8. Countdown Timer ⏱️
User inputs minutes or seconds, and the timer counts down to zero. Helps practice time.sleep().

9. Tip Calculator 🍽️
Calculate how much each person should pay including tip. Useful for string formatting and arithmetic.

10. Weather App (Using API) ☁️☀️🌧️
Use a public weather API to fetch real-time weather for a city. Great to explore APIs and the requests library.

For all resources and cheat sheets, check out my Telegram channel: https://t.me/pythonproz

Hope it helps :)

❤4

1.35K views11:10

Coding Free Books | Python | AI

❤5

1.41K views14:04

Coding Free Books | Python | AI

Top 10 machine Learning algorithms for beginners 👇👇

1. Linear Regression: A simple algorithm used for predicting a continuous value based on one or more input features.

2. Logistic Regression: Used for binary classification problems, where the output is a binary value (0 or 1).

3. Decision Trees: A versatile algorithm that can be used for both classification and regression tasks, based on a tree-like structure of decisions.

4. Random Forest: An ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model.

5. Support Vector Machines (SVM): Used for both classification and regression tasks, with the goal of finding the hyperplane that best separates the classes.

6. K-Nearest Neighbors (KNN): A simple algorithm that classifies a new data point based on the majority class of its k nearest neighbors in the feature space.

7. Naive Bayes: A probabilistic algorithm based on Bayes' theorem that is commonly used for text classification and spam filtering.

8. K-Means Clustering: An unsupervised learning algorithm used for clustering data points into k distinct groups based on similarity.

9. Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.

10. Gradient Boosting Machines (GBM): An ensemble learning method that builds a series of weak learners to create a strong predictive model through iterative optimization.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.me/datasciencefun

Like if you need similar content 😄👍

❤6

1.52K views09:58

Coding Free Books | Python | AI

HTML Handwritten Notes.pdf

19.9 MB

Html Handwritten Notes 📝

React "❤️" for more

❤8

1.48K views19:01

Coding Free Books | Python | AI

MERN STACK ROADMAP FOR BEGINNERS 2025

FRONTEND
HTML: ELEMENTS, TAGS, FORMS, SEMANTICS
CSS: SELECTORS, BOX MODEL, LAYOUT (FLEXBOX, GRID), RESPONSIVE DESIGN
BASIC WEB DEVELOPMENT TOOLS: VS CODE, CHROME DEVTOOLS

JAVASCRIPT (ES6+)
VARIABLES AND DATA TYPES
FUNCTIONS AND SCOPE
ARRAYS AND OBJECTS
PROMISES AND ASYNC/AWAIT
DOM MANIPULATION
EVENT HANDLING

FRONTEND DEVELOPMENT WITH REACT
BASICS OF REACT
JSX AND COMPONENTS
PROPS AND STATE
COMPONENT LIFECYCLE METHODS
FUNCTIONAL VS. CLASS COMPONENTS
EVENT HANDLING IN REACT

ADVANCED REACT
HOOKS: USESTATE, USEEFFECT, USECONTEXT, CUSTOM HOOKS
REACT ROUTER: NAVIGATION AND ROUTING
STATE MANAGEMENT: CONTEXT API, REDUX
PERFORMANCE OPTIMIZATION: REACT.MEMO, USEMEMO, USECALLBACK

UI LIBRARIES
CSS-IN-JS: STYLED-COMPONENTS, EMOTION
COMPONENT LIBRARIES: MATERIAL-UI, ANT DESIGN
BACKEND

BASICS OF NODE.JS
INTRODUCTION TO NODE.JS
NPM: PACKAGE MANAGEMENT
MODULES AND REQUIRE
FILE SYSTEM OPERATIONS

4. EXPRESS.JS
SETTING UP AN EXPRESS SERVER
MIDDLEWARE
ROUTING
HANDLING REQUESTS AND RESPONSES
ERROR HANDLING

DATABASE MANAGEMENT WITH MONGODB
BASICS OF MONGODB
NOSQL VS. SQL DATABASES
CRUD OPERATIONS
DATA MODELING AND SCHEMAS
INDEXES AND PERFORMANCE OPTIMIZATION

CONNECTING FRONTEND AND BACKEND
RESTFUL APIS
DESIGNING RESTFUL ENDPOINTS
CONSUMING APIS WITH FETCH/AXIOS
AUTHENTICATION AND AUTHORIZATION (JWT, OAUTH)
ERROR HANDLING AND STATUS CODES
.
FULL-STACK DEVELOPMENT

SETTING UP THE PROJECT STRUCTURE
CONNECTING REACT FRONTEND WITH EXPRESS BACKEND
STATE MANAGEMENT IN FULL-STACK APPS
PROJECTS
BEGINNER PROJECTS
TO-DO LIST APP
SIMPLE BLOG
WEATHER APP

INTERMEDIATE PROJECTS
E-COMMERCE SITE
SOCIAL MEDIA APP
REAL-TIME CHAT APPLICATION

ADVANCED PROJECTS
FULL-FEATURED CMS
PROJECT MANAGEMENT TOOL
COLLABORATIVE CODING PLATFORM.

Free Mernstack Resources For Web Developers: https://whatsapp.com/channel/0029Vaxox5i5fM5givkwsH0A

ENJOY LEARNING 👍👍

❤5

1.65K views07:31

About

Blog

Apps

Platform