LearnFlat · AI Applications (Interdisciplinary) · AI for Trading & Quant Finance

Financial Time-Series Forecasting

Predict future stock prices, volatility, and other financial metrics using models like ARIMA, GARCH, LSTMs, and Transformers.

34 courses

Recurrent Neural Networks and Sequence Modeling in Python

Learn to build and train LSTMs, GRUs, and recurrent neural networks in Python to forecast time series data and analyze natural language.

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Forecasting CO2 Emissions with ARIMA in Python

Learn to build reliable time series models using Python to project carbon emissions and support sustainability initiatives in the energy sector.

Foundations of Algorithmic Trading and Time Series in Python and R

Learn to analyze financial data, build predictive time series models, and implement algorithmic trading strategies using Python and R.

Time Series Analysis for Stock Market Forecasting in Python

Learn to analyze financial trends and build predictive models using Python, pandas, and modern time series techniques.

Forecasting CO2 Emissions with Deep Learning in Python

Build and evaluate deep learning models in Python to predict CO2 emissions and analyze environmental time-series data.

Stock Price Prediction with Deep Learning RNNs and LSTMs

Build, train, and evaluate recurrent neural networks and LSTM models to analyze and forecast financial market trends using modern Python libraries.

Python Basics for Time Series Analysis

Learn how to manipulate, clean, and prepare time-based data using Python and pandas, starting from absolute foundational concepts.

Stock Price Forecasting with LSTM and Bayesian Models in Python

Learn to build, evaluate, and deploy financial forecasting models using deep learning and probabilistic methods in Python, designed for beginners in quantitative analysis.

Time Series Forecasting and Prediction with ARIMA

Master the essentials of time series analysis, build predictive ARIMA models, and test your understanding with practical written exercises.

Foundations of LSTM Networks for Sequence Prediction

Learn to design, build, and train Long Short-Term Memory networks to model sequential and time-series data using modern deep learning practices.

Time Series Forecasting with Prophet in Python

Learn to analyze sequential data and build reliable predictive models using the powerful Prophet library in Python.

Time Series Forecasting with ARIMA Models

Learn the fundamentals of time series analysis and how to configure, evaluate, and apply ARIMA models to sequential data.

Sequential Deep Learning: Comparing LSTMs, Peephole Connections, and GRUs

Master recurrent neural network architectures by comparing standard LSTMs, peephole connections, and GRUs to choose the best model for sequential data.

Understanding Gated Recurrent Units (GRUs) for Sequential Data

Master the mechanics of GRUs, understand update and reset gates, and learn how to implement sequential deep learning models in Python for NLP and time series forecasting.

Sequential Data Modeling with LSTM Networks

Learn to build, configure, and optimize Long Short-Term Memory models for time-series and text prediction.

Time Series Stationarity: KPSS and Phillips-Perron Tests in Python

Learn how to detect and handle non-stationary time series data using KPSS and Phillips-Perron tests in Python to build reliable forecasting models.

Time Series Forecasting with Autoregressive AR Models in Python

Master the fundamentals of AR and ARIMA modeling to analyze and predict future trends using Python and statsmodels through structured written lessons.

Python Datetime: Build a Project Deadline Calculator

Master date arithmetic in Python using datetime and timedelta to build practical project deadline calculators and manage timelines.

LSTM Variants and Gated Recurrent Units for Sequence Modeling

Learn to implement and compare peephole LSTMs and GRUs to build efficient recurrent neural networks for text and sequence data.

Understanding LSTM Networks: A Practical Guide with Conceptual Exercises

Master the mechanics of Long Short-Term Memory networks, from core gate architectures to sequence modeling, through structured written explanations and self-assessments.

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