Machine Learning Techniques for Financial Forecasting
( 25 Modules )
Module #1 Introduction to Financial Forecasting Overview of financial forecasting, importance of accurate forecasting, and introduction to machine learning techniques
Module #2 Types of Financial Forecasting Exploring different types of financial forecasting, including time series, cross-sectional, and panel data forecasting
Module #3 Data Preprocessing for Financial Data preprocessing techniques for financial data, including handling missing values, outliers, and feature scaling
Module #4 Exploratory Data Analysis for Financial Data Exploratory data analysis techniques for financial data, including visualization and summary statistics
Module #5 Introduction to Machine Learning for Financial Forecasting Overview of machine learning, supervised and unsupervised learning, and regression analysis
Module #6 Linear Regression for Financial Forecasting Application of linear regression for financial forecasting, including model interpretation and evaluation
Module #7 Ridge Regression and Lasso Regression Regularization techniques for linear regression, including ridge regression and lasso regression
Module #8 Decision Trees for Financial Forecasting Introduction to decision trees, including model interpretation and feature importance
Module #9 Random Forest for Financial Forecasting Ensemble learning with random forest, including hyperparameter tuning and model evaluation
Module #10 Gradient Boosting for Financial Forecasting Introduction to gradient boosting, including XGBoost and LightGBM
Module #11 Neural Networks for Financial Forecasting Introduction to neural networks, including multilayer perceptrons and recurrent neural networks
Module #12 Long Short-Term Memory (LSTM) Networks Application of LSTM networks for financial time series forecasting
Module #13 Convolutional Neural Networks (CNNs) for Financial Forecasting Application of CNNs for financial forecasting, including image-based and signal-based forecasting
Module #14 Clustering for Financial Forecasting Introduction to clustering techniques, including k-means and hierarchical clustering
Module #15 Dimensionality Reduction for Financial Data Introduction to dimensionality reduction techniques, including PCA and t-SNE
Module #16 Model Evaluation and Selection Metrics for evaluating financial forecasting models, including mean absolute error and mean squared error
Module #17 Walk-Forward Optimization for Financial Forecasting Walk-forward optimization technique for model evaluation and selection
Module #18 Handling Imbalanced Data in Financial Forecasting Techniques for handling imbalanced data in financial forecasting, including undersampling and oversampling
Module #19 Big Data and Distributed Computing for Financial Forecasting Introduction to big data and distributed computing for financial forecasting, including Hadoop and Spark
Module #20 Financial Forecasting with Python Implementation of machine learning techniques for financial forecasting using Python
Module #21 Financial Forecasting with R Implementation of machine learning techniques for financial forecasting using R
Module #22 Case Study:Stock Price Forecasting Application of machine learning techniques for stock price forecasting
Module #23 Case Study:Credit Risk Assessment Application of machine learning techniques for credit risk assessment
Module #24 Case Study:Portfolio Optimization Application of machine learning techniques for portfolio optimization
Module #25 Course Wrap-Up & Conclusion Planning next steps in Machine Learning Techniques for Financial Forecasting career
