by Angie Ma, Alessandra Stagliano, Gary Willis | Released August 2017 | ISBN: 9781492023920

Classification is the sub-field of machine learning encountered more frequently than any other in data science applications. There are many different classification techniques and this course explains some of the most important ones, including algorithms such as logistic regression, k-nearest neighbors (k-NN), decision trees, ensemble models like random forests, and support vector machines. The course also covers Naive Bayes classifiers and in so doing, covers Bayes' theorem and basic Bayesian inference, both of which are widely used in training many machine learning algorithms. A basic knowledge of algebra is required. A solid understanding of differential calculus will be necessary for logistic regression, Support Vector Machines and Bayesian Inference. Understand what a classification algorithm is and when it is appropriate to use one Learn about logistic regression, k-NN, decision trees, random forests, SVMs, and Naive Bayes classifiers Discover why optimization is important in many ML algorithms Gain a high level understanding of how gradient descent works Learn how to use — and enjoy free access to — the SherlockML data science platform Develop the skills required for the machine learning job market, where demand outstrips supplyAngie Ma, Gary Willis, and Alessandra Stagliano are data scientists with ASI Data Science, a London based AI/machine learning solutions firm. Angie co-founded ASI and is also the founder of Data Science Lab London, one of the biggest communities of data scientists and data engineers in Europe, with over 2,500 members. Angie holds a PhD in physics from London's University College, Gary Willis holds a PhD in statistical physics from London's Imperial College, and Alessandra Stagliano holds a PhD in computer science from the University of Genoa. Collectively, the group has worked on over 150 commercial AI/machine learning projects. Show and hide more

1. Logistic regression
   • Introduction 00:02:08
   • Classification 00:03:27
   • Logistic Regression 00:04:49
   • Fitting Logistic Regression 00:06:15
   • Gradient Descent 00:06:19
   • Multivariate and Non-Linear Logistic Regression 00:05:07
   • Multi Class Classification 00:02:34
2. KNN, Trees and Ensemble models
   • KNN 00:11:32
   • Decision Trees 00:18:56
   • Ensemble Models & the Random Forest 00:07:13
3. Support Vector Machines
   • Introduction 00:04:29
   • How to maximize the margin 00:03:54
   • Soft vs hard margin 00:03:12
   • Non-linear SVMs 00:02:57
   • Kernels 00:01:57
4. Bayes and Naive Bayes
   • Conditional Probability & Bayes Theorem 00:10:33
   • Bayesian Inference 00:07:12
   • Naïve Bayes and Conditional Independence 00:13:19
5. Conclusion
   • Conclusions 00:01:42
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