Proactive, hard-working in nature and delicate for quality of solution and performance. Finding passion & motivation in challenges relating to creative problem-solversView Profile on LinkedIn
A fast-paced introduction to using Python for basic machine learning. Topics covered are decision trees, linear/logistic regression, and random forest classifiers - all the basics you need to know to set up your first machine learning models.
Anyone with a knowledge of how to operate a computer and search for things on Google. Programming knowledge is always encouraged, but not necessary. For extra reference, feel free to investigate a few articles online about programming (your specific course will offer specific pre-material).
In this advanced course, we will dive deep into Machine Learning. It is structured around: Model selection: Grid Search, k-fold Cross Validation, Parameter Tuning. Advanced Classification Algorithms: K-NN, SVM, Kernel SVM, Naive Bayes. Unsupervised Learning: K-Means Clustering, Hierarchical Clustering.
ML Level 1 Completion