Overview
Machine Learning made simple with Excel! Regression models for advanced data analysis & business intelligence (no code!)
Syllabus
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- Introduction to Machine Learning for Data Analysis
-- Overview of Machine Learning
-- Importance of Data Analysis in Business and Research
-- Types of Machine Learning: Supervised vs Unsupervised Learning
- Fundamentals of Regression Analysis
-- Introduction to Regression
-- Linear Regression
-- Assumptions of Linear Regression
-- Evaluating Linear Models (R², RMSE, MAE)
-- Multiple Linear Regression
-- Polynomial Regression
- Advanced Regression Techniques
-- Ridge Regression
-- Lasso Regression
-- Elastic Net Regression
-- Non-linear Regression Models
-- Support Vector Regression (SVR)
- Time Series Analysis and Forecasting
-- Introduction to Time Series Data
-- Components of Time Series: Trend, Seasonality, and Noise
-- Time Series Decomposition
-- Moving Averages and Smoothing Techniques
-- Autoregressive Models (AR, MA, ARMA)
-- ARIMA Models and Seasonal ARIMA (SARIMA)
-- Evaluating Forecast Models (MAPE, AIC, BIC)
- Machine Learning for Time Series Forecasting
-- Introduction to Machine Learning in Time Series
-- Feature Engineering for Time Series
-- Regression Models for Time Series
-- Tree-based Models: Decision Trees, Random Forests, and Gradient Boosting
-- Neural Networks for Time Series Forecasting
- Model Evaluation and Selection
-- Cross-Validation Techniques
-- Overfitting and Underfitting
-- Hyperparameter Tuning using Grid Search and Random Search
-- Model Deployment Considerations
- Tools and Libraries for Regression & Forecasting
-- Python Libraries: NumPy, pandas, scikit-learn
-- Time Series-specific Tools: statsmodels, Prophet
-- Introduction to R for Time Series Analysis
- Case Studies and Applications
-- Academic Case Study: Housing Price Prediction
-- Business Case Study: Sales Forecasting Using Historical Data
-- Environmental Case Study: Climate Data Analysis
- Project: Developing a Forecasting Model
-- Project Introduction and Requirements
-- Data Collection and Preprocessing
-- Model Selection and Training
-- Evaluation and Presentation of Results
- Conclusion and Further Reading
-- Review of Key Concepts
-- Recommended Resources for Further Learning
-- Future Trends in Machine Learning for Data Analysis
Taught by
Maven Analytics and Joshua MacCarty
Tags
On-Demand
