107.36 MB | 00:02:40 | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English
Files Included :
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)
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)
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