Support Vector Machines

A foundational book on kernel methods, providing a comprehensive overview of the theory and algorithms. While not a course, it is a key resource for in-depth learning.

A free online course designed for both beginners and professionals, covering the fundamentals of Support Vector Machines with solved problems and examples.

Interested in Machine Learning and Deep Learning ? Then this course is for you!This course is about the fundamental concepts of machine learning, deep learning, reinforcement learning and machine learning. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking.In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with Sk Learn, Keras and TensorFlow. MACHINE LEARNING Linear Regressionunderstanding linear regression modelcorrelation and covariance matrixlinear relationships between random variablesgradient descent and design matrix approaches Logistic Regressionunderstanding logistic regressionclassification algorithms basicsmaximum likelihood function and estimationK-Nearest Neighbors Classifierwhat is k-nearest neighbour classifier?non-parametric machine learning algorithms Naive Bayes Algorithmwhat is the naive Bayes algorithm?classification based on probabilitycross-validation overfitting and underfitting Support Vector Machines (SV Ms)support vector machines (SV Ms) and support vector classifiers (SV Cs)maximum margin classifierkernel trick Decision Trees and Random Forestsdecision tree classifierrandom forest classifiercombining weak learners Bagging and

Introduction Introduction of the Course Introduction to Machine Learning and Deep Learning Introduction to Google Colab Python Crash Course Data Preprocessing Supervised Machine Learning Regression Analysis Logistic RegressionK-Nearest Neighbor (KNN)Bayes Theorem and Naive Bayes Classifier Support Vector Machine (SVM)Decision Trees Random Forest Boosting Methods in Machine Learning Introduction to Neural Networks and Deep Learning Activation Functions Loss Functions Back Propagation Neural Networks for Regression Analysis Neural Networks for Classification Dropout Regularization and Batch Normalization Convolutional Neural Network (CNNs)Recurrent Neural Network (RNNs)Autoencoders Generative Adversarial Network (GANs)Unsupervised Machine LearningK-Means Clustering Hierarchical Clustering Density Based Spatial Clustering Of Applications With Noise (DBSCAN)Gaussian Mixture Model (GMM) Clustering Principal Component Analysis (PCA)What you’ll learn Theory, Maths and Implementation of machine learning and deep learning algorithms.Regression Analysis.Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.Build Artificial Neural Networks and use them for Regression and Classification Problems.Using GPU with Deep Learning Models.Convolutional Neural Networks Transfer Learning Recurrent Neural Networks Time series forecasting and classification.Autoencoders Generative Adversarial Networks Python from scr

Academy of Computing & Artificial Intelligence proudly present you the course "Data Engineering with Python". It all started when the expert team of Academy of Computing & Artificial Intelligence (PhD, PhD Candidates, Senior Lecturers , Consultants , Researchers) and Industry Experts . hiring managers were having a discussion on the most highly paid jobs & skills in the IT/Computer Science / Engineering / Data Science sector in 2021. At the end of the Course you will be able to start your career in Data Mining & Machine Learning. 1) Introduction to Machine Learning - [A -Z] Comprehensive Training with Step by step guidance2) Setting up the Environment for Machine Learning - Step by step guidance  [R Programming & Python]3) Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines (SVM), Random Forest)4) Unsupervised Learning5) Convolutional Neural Networks - CNN6) Artificial Neural Networks 7) Real World Projects with Source Course Learning Outcomes To provide awareness of  (Supervised & Unsupervised learning) coming under Machine Learning (Why we need Data Mining & Machine Learning, What is Data Mining, What is Machine Learning, Traditional Programming Vs Machine Learning, Steps to Solve a Data Mining & Machine Learning Problem, Classification , Clustering)Describe intelligent problem-solving methods via appropriate usage of Machine Learning techniques.To build appropriate neural models from using state-of-the-art python framework.To setup the Environment for Machine Learning - Step by step guidance  [R Progra

Ready to master machine learning in Python and launch your career in data science? This hands-on, comprehensive course is the definitive guide to becoming a skilled practitioner, taking you from the fundamentals of Scikit-Learn to building powerful, real-world AI models.You'll gain a deep understanding of Scikit-Learn, Python's most essential and widely used machine learning library. By focusing on practical application, you will not only learn the algorithms but also how to implement the full data science workflow—a critical skill for employers.Master the Complete Data Science and Machine Learning Workflow This masterclass will teach you to:Prepare and Preprocess complex, real-world datasets using Python (Pandas & Num Py) and the integrated tools within Scikit-Learn.Build Powerful Models using core Machine Learning algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SV Ms).Optimize Performance with advanced techniques like Regularization, Cross-Validation, and Principal Component Analysis (PCA) for Dimensionality Reduction.Apply both Supervised and Unsupervised Learning to solve diverse business problems in data science.Understand the AI Landscape by covering the basics of Neural Networks and their role in Deep Learning.Work through short coding exercises and large, project-style assignments, mirroring the daily work of a professional data scientist.Why Learn Machine Learning with Us?We're

Welcome to the exciting world of Matrix Calculus, a fundamental tool for understanding and solving problems in machine learning and data science. In this course, we will dive into the powerful mathematics that underpin many of the algorithms and techniques used in these fields. By the end of this course, you'll have the knowledge and skills to navigate the complex landscape of derivatives, gradients, and optimizations involving matrices.Course Objectives:Understand the basics of matrix calculus, linear and quadratic forms, and their derivatives.Learn how to utilize the famous Matrix Cookbook for a wide range of matrix calculus operations.Gain proficiency in optimization techniques like gradient descent and Newton's method in one and multiple dimensions.Apply the concepts learned to real-world problems in machine learning and data science, with hands-on exercises and Python code examples.Why Matrix Calculus? Matrix calculus is the language of machine learning and data science. In these fields, we often work with high-dimensional data, making matrices and their derivatives a natural representation for our problems. Understanding matrix calculus is crucial for developing and analyzing algorithms, building predictive models, and making sense of the vast amounts of data at our disposal.Section 1: Linear and Quadratic Forms In the first part of the course, we'll explore the basics of linear and quadratic forms, and their derivatives. The linear form appears in all of the most fundamental and popular machine learning models, including linear regression, logistic regression, support vector machine (SVM), and deep neural networks. We will also dive into quadratic forms, which are fundamental to understanding optimization problems, which appear in regression, portfolio optimization in finance, signal processing, and control theory.The Mat