42elenz / alfa_training_weiterbildung

Alfa_Training

Chapter One: Introduction

In this GitHub repository, I document my journey through a professional training program in Statistics, Python programming, and Machine Learning, spanning three months. The purpose of this training was to equip myself with the necessary skills and knowledge to excel in the field of Machine Learning.

Chapter Two: Python Programming

During the initial phase of my training, I delved into the world of Python programming. This included mastering fundamental concepts such as writing functions, understanding object-oriented programming (OOP) principles, implementing error handling, and even creating graphical user interfaces (GUI). To showcase my progress, I completed a final project where I developed a web crawler capable of navigating PubMed. This crawler utilizes Natural Language Processing (NLP) techniques to extract and filter relevant words and information.

This is a screenshot of my final pubcrawler

Chapter Three: Statistics

In the second phase of my training, I focused on statistical methods. I began with the basics, building a solid foundation in concepts such as probability, descriptive statistics, and hypothesis testing. I then explored more advanced topics, including statistical analysis between two groups and multiple groups. As part of my coursework, I tackled several practical examples to apply and reinforce my understanding of these statistical methods.

Here you can see a screenshot of the result of an anlyses I did.

Chapter Four: Machine Learning

In the final phase of my training, I immersed myself in the world of Machine Learning. I covered the essentials, including supervised and unsupervised learning techniques, as well as regression and classification algorithms. Additionally, I delved into model evaluation and hyperparameter tuning, employing methods like grid search to optimize model performance. Data preprocessing also played a crucial role in my training, as I learned how to prepare datasets for machine learning applications.

Here you can see the outcomes of the different type of models trained.