1 Introduction (8 MB) 2 What you should know (1.75 MB) 1 Recap of Python (3.31 MB) 2 Recap of machine learning (5.25 MB) 3 Data cleaning and preparation (4.02 MB) 4 Challenge- How would you solve this problem with ML (1.56 MB) 5 Solution- How would you solve this problem with ML (6.45 MB) 1 Introduction to machine learning concepts (2.86 MB) 2 Building regression models in Excel (7.88 MB) 3 Classification models for business data (8.06 MB) 4 Challenge- Build a regression model (1.37 MB) 5 Solution- Build a regression model (5.19 MB) 1 Predictive analytics for financial forecasting (8.77 MB) 2 Automated decision-making processes (5.06 MB) 3 Real-time data processing and analysis (3.49 MB) 4 Challenge- Create a financial forecast (1.05 MB) 5 Solution- Create a financial forecast (4.5 MB) 1 Data manipulation with pandas (4.18 MB) 2 Numerical analysis with NumPy (4.08 MB) 3 Machine learning with scikit-learn (3.28 MB) 1 Linear regression for business insights (4.98 MB) 2 Random forests for predictive modelling (4.08 MB) 3 Clustering techniques for market segmentation (5.96 MB) 1 Next steps (1.99 MB) Ex Files Advanced Python Excel (265.57 KB)