Learn the complete quant trading workflow and use machine learning algortihms to develop good trading strategies. The course is designed to fully immerse you into the complete quantitative trading workflow, going from hypothesis generation to data preparation, feature engineering and training testing of multiple machine learning algorithms (backtesting). It is a bootcamp designed to get you from zero to hero. The course is aimed at teaching about trading, giving you understanding of the differences between discretionary and quantitative trading. You will learning about different trading instruments/products or also known as asset classes.

Online Courses Udemy Introduction to Machine Learning & Deep Learning in Python, Regression, Naive Bayes Classifier, Support Vector Machines, Random Forest Classifier and Deep Neural Networks Created by Holczer Balazs Students also bought Cluster Analysis and Unsupervised Machine Learning in Python Feature Engineering for Machine Learning Data Science 2020: Complete Data Science & Machine Learning Machine Learning A-Z: Become Kaggle Master Python for Time Series Data Analysis Ensemble Machine Learning in Python: Random Forest, AdaBoost Preview this course GET COUPON CODE Description This course is about the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very good guess about stock prices movement in the market. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with SkLearn, Keras and TensorFlow.

In machine learning, we mainly deal with two kinds of problems that are classification and regression. There are several different types of algorithms for both tasks. But we need to pick that algorithm whose performance is good on the respective data. Ensemble methods like Random Forest, Decision Tree, XGboost algorithms have shown very good results when we talk about classification. These algorithms give high accuracy at fast speed.

Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it's simplicity and the fact that it can be used for both classification and regression tasks. In this post, you are going to learn, how the random forest algorithm works and several other important things about it. Random Forest is a supervised learning algorithm. Like you can already see from it's name, it creates a forest and makes it somehow random.

Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it's simplicity and the fact that it can be used for both classification and regression tasks. In this post, you are going to learn, how the random forest algorithm works and several other important things about it. Random Forest is a supervised learning algorithm. Like you can already see from it's name, it creates a forest and makes it somehow random.