NextGen AI
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Forecasting & Predictive Analytics with AI

Build robust predictive models and leverage AI for accurate forecasting across diverse business scenarios.

4.8
(3,200)
3,200 studentsEnglish

Created by Alex Rivera · Full-Stack AI Engineer

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$789$1,149Save 31%
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This course includes

  • 15 video lessons
  • 4h 38m of content
  • Downloadable resources
  • Full lifetime access
  • Certificate of completion
  • 30-day money-back guarantee

What you'll learn

  • Understand the principles of time-series analysis and forecasting.
  • Implement statistical forecasting models (ARIMA, ETS).
  • Apply machine learning regression techniques for prediction.
  • Build and train Recurrent Neural Networks (RNNs) for time-series data.
  • Utilise Long Short-Term Memory (LSTM) networks for complex patterns.
  • Perform feature engineering specific to predictive modelling.
  • Evaluate and compare the performance of different forecasting models.
  • Interpret model results and communicate predictions effectively.

About this course

Go beyond simple trend analysis and master the art of prediction. This advanced course delves into the sophisticated techniques and AI algorithms essential for accurate forecasting and predictive modelling. You'll learn to tackle complex time-series data, understand the nuances of different forecasting models, and implement machine learning approaches to predict future events with confidence. We'll cover everything from traditional statistical methods like ARIMA and Exponential Smoothing to modern AI techniques including Recurrent Neural Networks (RNNs) and LSTMs. You'll gain hands-on experience applying these models to real-world problems, such as predicting stock prices, customer churn, equipment failure, or sales volumes. Emphasis will be placed on model evaluation, feature engineering, and understanding the limitations and assumptions inherent in predictive analytics. Upon completion, you'll possess the skills to develop, deploy, and interpret advanced predictive models. This course is designed for those who want to harness the power of AI to anticipate the future, optimise resource allocation, and gain a significant competitive advantage through data-driven foresight.

Course content

5 modules · 15 lessons · 4h 38m

  • Understanding Time Series ComponentsPreview
    18m
  • Stationarity and Data TransformationsPreview
    20m
  • Autocorrelation and Partial Autocorrelation (ACF/PACF)
    15m

Requirements

  • Strong Python programming skills and experience with libraries like Pandas and NumPy.
  • Solid understanding of machine learning fundamentals (supervised learning, evaluation metrics).
  • Familiarity with deep learning concepts is recommended.
  • Experience with data visualisation libraries.

Your instructor

A

Alex Rivera

Full-Stack AI Engineer

Alex has built and launched a dozen AI products and teaches developers to ship with LLMs the pragmatic way.