Inside the course, you'll learn how to:

• Set up a Python development environment correctly
• Gain complete machine learning toolsets to tackle most real-world problems
• Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
• Combine multiple models with by bagging, boosting or stacking
• Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data • Develop in Jupyter (IPython) notebook, Spyder and various IDE
• Communicate visually and effectively with Matplotlib and Seaborn
• Engineer new features to improve algorithm predictions
• Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
• Use SVM for handwriting recognition, and classification problems in general
• Use decision trees to predict staff attrition
• Apply the association rule to retail shopping datasets

• Solve any problem in your business or job with powerful Machine Learning models.
• Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.