Machine Learning for Trading
Apply machine learning models, including regression, classification, and reinforcement learning, to predict market movements and generate trading signals.
84 courses
Learn to build, train, and deploy machine learning models directly in the browser using JavaScript, even if you have no prior data science experience.
Learn the core principles of machine learning and use Python with scikit-learn to build a predictive model that estimates real estate values.
Build practical machine learning and neural network models using Python to solve real-world prediction, classification, and natural language processing challenges.
Build, test, and deploy predictive models for financial markets using supervised, unsupervised, and reinforcement learning techniques with Python.
Learn to build and interpret regression models in Python, moving from basic statistical analysis to predictive machine learning workflows.
Build a solid foundation in Python coding and use TensorFlow to create regression models that analyze data trends and automate predictive tasks.
Build and train neural networks and decision tree ensembles using TensorFlow to solve complex, real-world classification and regression problems.
Master foundational regression techniques to predict real-world continuous data, from housing prices to financial trends, using clear Python examples.
Learn to transform raw data into high-quality inputs using BigQuery ML, Keras, and TensorFlow to improve model accuracy and performance.
Apply your Python machine learning skills to design, build, and evaluate a content-based recommendation engine using scikit-learn and TensorFlow.
Master the art of analyzing and forecasting temporal data to build predictive models for finance, health, and environmental signals.
Apply machine learning techniques to financial datasets to automate analysis, predict market trends, and make data-driven investment decisions.
Build, train, and evaluate key machine learning models using Python and Keras through clear, written explanations and practical code walkthroughs.
Learn to build and evaluate predictive classification models using Python, from foundational probability concepts to real-world implementation.
Build a solid foundation in data analysis, predictive modeling, and neural network design using modern Python libraries and industry-standard workflows.
Learn to build, train, and evaluate machine learning models for real-world engineering and technical data analysis using MATLAB.
Build a strong foundation in machine learning using Python, moving from basic supervised algorithms to neural networks and modern generative concepts.
Build a solid foundation in machine learning by reading structured explanations and applying core algorithms using Python, Scikit-learn, and Tensorflow.
Build a solid foundation in predictive modeling, deep learning, and data preprocessing using Python to prepare for a career in machine learning science.
Learn to preprocess data, construct robust regression and classification pipelines, and optimize model performance using Python, Pandas, and scikit-learn.
Showing 20 of 84 courses