Machine Learning: Beginner to Expert What you'll learn: Machine Learning Supervised learning Unsupervised learning Model and cost function Parameter learning Linear algebra Linear regression Linear regression with multiple variables Gradient descent Features and polynomial regression Computing parameters analytically Logistic regression Classification and representation Hypothesis representation Decision boundary Multi class classification Regularisation Neural networks Non linear hypothesis Back propagation Evaluating a learning algorithm Advice for applying machine learning Bias vs variance Learning curves Building a spam classifier Machine learning system design Handling skewed data Using large data sets Support vector machines Large margin classification Kernels SVM in practice Clustering Dimensionality reduction Motivation Principal component analysis Applying PCA Anomaly detection Density estimation Gaussian distribution Building an anomaly detection system Recommender systems Predicting movie ratings Collaborative filtering Low rank matrix factorisation Large scale machine learning Gradient descent with large data sets Advanced topics Online learning Map reduction and parallelism Photo OCR Requirements No prior knowledge required Description Welcome to Machine Learning! In this course we start at the very beginning by defining Machine Learning before we dive into it and get more practical with it. By the end of this course you will be an expert in Machine Learning because in this course we do not leave a single stone unturned. To start with, we introduce the core idea of teaching a computer to learn concepts using data; without being explicitly programmed. We end by looking at an application example. Everything else in between will teach you everything you need to know. Who this course is for Machine learning and AI
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