In Part 1 we'll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We'll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin and box plots, scatter plots, and correlation: Variable types, empty values, range and count calculations, left/right censoring, etc. Histograms, frequency tables, mean, median, mode, variance, skewness, etc. Throughout the course, we'll introduce real-world scenarios to solidify key concepts and simulate actual data science and business intelligence cases. You'll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and more. In Part 2 we'll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting.

We are entering a phase of massive digital transformation powered by the use of AI systems & Machine Learning algorithms. These technology enablers are seeing an exponential growth in enterprise level solutions. According to some estimates AI systems will top $46 billion by 2020. The only hindrance in the adoption of this revolutionary technology has been the skills gap & the lack of familiarity, but with the increased spending on R&D and continued training should address this problem sooner than later. For connected consumers Machine Learning presents an interesting opportunity to enable on demand services.