Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model Make use to unsupervised Machine Learning (ML) algorithms to understand your dataĭevelop in Jupyter (IPython) notebook, Spyder and various IDEĬommunicate visually and effectively with MatDescriptionlib and SeabornĮngineer new features to improve algorithm predictions Understand the various regression, classification and other ml algorithms performance metricsĬombine multiple models with by bagging, boosting or stacking Gain complete machine learning tool sets to tackle most real world problems
Set up a Python development environment correctly By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more! You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.
With over 18 hours of content and more than fifty 5 star rating, it's already the longest and best rated Machine Learning course on Udemy!īuild Powerful Machine Learning Models to Solve Any Problem The average salary of a Machine Learning Engineer in the US is $166,000 ! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real life problems in your business, job or personal life with Machine Learning algorithms.Ĭome learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singapore who has followed Rob Percival's "project based" teaching style to bring you this hands-on course. Static plots using matplotlib and seaborn libraries.Īnimations, dynamic plots using altair libraryĭownload Files 7.Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More. If/else, loops, iterators and generators, error handling.įunctions, decorators, classes, inheritance, decorators inside classes.Īrrays and Matrices, reading files, DataFrame, Series, pivot tables, group by, pipelines, datetime objects. Programming language features, VS Code, Jupyter Notebook/Lab (Colab), virtual environments, variables, data types, lists and dictionaries. He is currently working at a well-known Italian insurance company as a data scientist and Non-Life technical provisions evaluator. He graduated in physics and statistical and actuarial sciences. Fabio Mardero is a data scientist from Italy.
0 kommentar(er)
