Kurs: Machine Learning

Machine Learning course with practical examples

Welcome to the machine learning course that focuses on basic concepts and real-world examples, particularly predicting stock prices based on Yahoo Finance data. This repository is a dynamic document that will continue to develop as the course progresses.

The course will follow a predicting stock prices use case, and you will learn the essential concepts and techniques of machine learning for stock price prediction.

Throughout this course, we will cover the fundamental concepts and techniques of machine learning, including supervised, unsupervised, and reinforcement learning. We will also provide real-world examples to help you better understand how these techniques are used in practice. Additionally, we will introduce you to tools and libraries commonly used in machine learning, such as Python, pandas, and scikit-learn.

Please note that this repository will be updated frequently as we progress through the course, so be sure to check back regularly for updates. At the end of the course, there will be a final test to evaluate your understanding of the material. We hope you find this course informative and engaging!

Introduction to Machine Learning for Stock Price Prediction

Hello and welcome to this introduction to machine learning course! In this course, our objectives are to provide you with a basic understanding of machine learning concepts and techniques, introduce you to popular machine learning algorithms, and show you how they can be applied to real-world problems.
Module 1 Pakiet SCORM
External resources list Plik

Preparing Stock Market Data for Machine Learning

In this module you'l learn how to retrieve and preprocess stock market data using Python libraries such as pandas and yfinance.
Goals:
learn how to retrieve and preprocess stock market data using Python libraries such as pandas and yfinance.
Explore different feature engineering techniques to prepare stock market data for machine learning. Integrate external data sources such as news articles and social media sentiment analysis to improve stock price prediction models. Understand how to clean and transform stock market data to prepare it for machine learning.
Prerequisites
Basic knowledge of Python programming language. Familiarity with data structures such as lists and dictionaries in Python. Basic understanding of machine learning concepts.
Module 2 Pakiet SCORM
data Folder

Introduction to Machine Learning Models for Stock Price Prediction

In this section, we will compare two approaches for predicting stock prices: LSTM and linear regression. LSTM (Long short-term memory) is a type of recurrent neural network that can capture long-term dependencies in sequential data, while linear regression is a simple but powerful regression model that can fit a linear relationship between the features and the target variable. Both approaches have been used successfully for stock price prediction, but they differ in their ability to handle temporal dependencies and their complexity. In this section, we will discuss the advantages and disadvantages of each approach, and compare their performance on a real-world dataset. By the end of this section, you will have a better understanding of which approach is best suited for your use case.
Module 3 Pakiet SCORM
data Folder

Training and Deploying Machine Learning Models

In this section, we will explain how to split the data into training and testing sets for both approaches, and how to train the models using the training data. Before training a machine learning model to predict stock prices, it is important to split the data into training and testing sets. The training set is used to train the model, while the testing set is used to evaluate the performance of the trained model.
Module 4 Pakiet SCORM
data Folder

Machine Learning for Stock Price Prediction: Final Test

Final Test