In this training course, you'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen. By the end of the course, you will have trained machine learning algorithms to classify flowers, predicting house prices, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells, and much more!
At the end of this course the participants will be able to:
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
Make use of 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
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, pre-vision, recall, etc., and when to use them.
Combine multiple models by bagging, boosting, or stacking
Anyone willing and interested to learn machine learning algorithms with Python
Anyone who has a deep interest in the practical application of machine learning to real-world problems
Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms
Any intermediate to advanced EXCEL users who is unable to work with large datasets
Anyone interested to present their findings in a professional and convincing manner
Anyone who wishes to start or transit into a career as a data scientist
Anyone who wants to apply machine learning to their domain
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