Dive into the exciting world of Machine Learning in Trading with a focus on Tesla ($TSLA) stock. This learning path is designed to take you from beginner to pro, teaching you how to analyze financial data, build predictive models, and make smart trading decisions.
Overview
Syllabus
- Basic $TSLA Financial Data Handling in Pandas
- This course introduces the fundamental concepts of data handling using Pandas in Python. Designed for beginners, it covers the basics of loading data, performing simple data manipulations, and basic visualizations. Through working with the Tesla stock dataset, you will gain the foundational skills needed to manipulate and visualize financial time series data effectively.
- Technical Indicators in Financial Analysis with Pandas
- In this course, you will delve into the construction and application of technical indicators commonly used in financial analysis. Working with the Tesla stock dataset, you will apply moving averages and exponential moving averages, and understand how these indicators can be used to make informed trading decisions.
- Preparing Financial Data for Machine Learning
- This course explores the essential steps for preparing data for machine learning, focusing specifically on financial time series data. From feature engineering to scaling and train-test splitting, you will learn to apply best practices in preprocessing data to pave the way for successful model training and evaluation.
- Introduction to Machine Learning with Gradient Boosting Models
- This course aims to introduce you to building and understanding gradient boosting models through practical application in financial market predictions. It centers on using the Gradient Boosting Regressor to forecast price changes in Tesla stock, encompassing model training, hyperparameter tuning, and evaluation.
Courses
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This course introduces the fundamental concepts of data handling using Pandas in Python. Designed for beginners, it covers the basics of loading data, performing simple data manipulations, and basic visualizations. Through working with the Tesla stock dataset, you will gain the foundational skills needed to manipulate and visualize financial time series data effectively.
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In this course, you will delve into the construction and application of technical indicators commonly used in financial analysis. Working with the Tesla stock dataset, you will apply moving averages and exponential moving averages, and understand how these indicators can be used to make informed trading decisions.
-
This course explores the essential steps for preparing data for machine learning, focusing specifically on financial time series data. From feature engineering to scaling and train-test splitting, you will learn to apply best practices in preprocessing data to pave the way for successful model training and evaluation.
-
This course aims to introduce you to building and understanding gradient boosting models through practical application in financial market predictions. It centers on using the Gradient Boosting Regressor to forecast price changes in Tesla stock, encompassing model training, hyperparameter tuning, and evaluation.
Related Courses
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Technical Indicators in Financial Analysis with Pandas
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Basic $TSLA Financial Data Handling in Pandas
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Introduction to Machine Learning with Gradient Boosting Models
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Tesla Stock Price Prediction using Facebook Prophet
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Machine Learning with Python: Zero to GBMs
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Introduction to Trading, Machine Learning & GCP
