HOW TO BECOME PROGRAMMER: From Beginner to Real IT Industry

Develop discipline before motivation by building a four-part habit. Plan a daily small step, create what you want, actually produce it, and reward yourself within a clean environment.

Apply the 20 percent learning framework to begin your journey in machine learning and data science with projects and resources like tech with Tim, Stanford classes, and neural nine.

Start small, start where you are, and build a daily programming habit by finishing small projects you love. Then collaborate with a group of fellow programmers for future teamwork.

Publish your projects to GitHub with clear code, post videos on YouTube to show what you created, and share on LinkedIn to gather feedback and attract job opportunities.

Join a supportive tribe of programmers and engineers on Discord, quit the old mindset, start projects, help others, trust the process, and level up the business.

Scrape league of legends team data with beautifulsoup and requests, prepare and combine stats, and build an end-to-end python machine learning project to predict the world championship outcomes.

scrape League of Legends data to build a training dataset and a labeled data csv, then create and merge pandas data frames to prepare inputs for a machine learning model.

Explore the correlation between gold per minute and final placement in League of Legends by merging match and leaderboard data frames, extracting common themes, and visualizing with matplotlib in Python.

Learn to prepare and merge two data sets for a League of Legends python machine learning project. Extract features and placement target, then normalize and convert data for model training.

Use gradient descent in a multi-variant linear regression to predict League of Legends world standings from input features. Explain hypothesis, coefficients, learning rate, iterations, and testing.