{"id":1717,"date":"2019-02-13T06:32:36","date_gmt":"2019-02-13T06:32:36","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2019\/02\/13\/learn-machinelearning-coding-basics-in-a-weekend-glossary-and-mindmap\/"},"modified":"2019-02-13T06:32:36","modified_gmt":"2019-02-13T06:32:36","slug":"learn-machinelearning-coding-basics-in-a-weekend-glossary-and-mindmap","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2019\/02\/13\/learn-machinelearning-coding-basics-in-a-weekend-glossary-and-mindmap\/","title":{"rendered":"Learn #MachineLearning Coding Basics in a weekend &#8211; Glossary and Mindmap"},"content":{"rendered":"<p>Author: ajit jaokar<\/p>\n<div>\n<p>For background to this post, please see <a href=\"https:\/\/www.datasciencecentral.com\/profiles\/blogs\/learn-machinelearning-coding-basics-in-a-weekend-a-new-approach\" target=\"_blank\" rel=\"noopener\">Learn #MachineLearning Coding Basics in a weekend<\/a>. Here,we present the glossary that we use for the coding\u00a0and the mindmap attached to these classes and upcoming book.\u00a0<\/p>\n<p><a href=\"https:\/\/storage.ning.com\/topology\/rest\/1.0\/file\/get\/1028923913?profile=original\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" src=\"https:\/\/storage.ning.com\/topology\/rest\/1.0\/file\/get\/1028923913?profile=RESIZE_710x\" class=\"align-full\"><\/a><\/p>\n<p>The following entries (see below) are part of the glossary. The glossary is available as a PDF document. You can download it <a href=\"https:\/\/storage.ning.com\/topology\/rest\/1.0\/file\/get\/1030381124?profile=original\" target=\"_blank\" rel=\"noopener\">here<\/a>.<\/p>\n<p><span style=\"font-size: 14pt;\"><strong>Contents<\/strong><\/span><\/p>\n<p><strong>Machine Learning concepts 4<\/strong><\/p>\n<ul>\n<li>Learning Algorithm 4<\/li>\n<li>Predictive Model (Model) 4<\/li>\n<li>Model, Classification 4<\/li>\n<li>Model, Regression 4<\/li>\n<li>Representation Learning 4<\/li>\n<li>Supervised Learning 4<\/li>\n<li>Unsupervised Learning 4<\/li>\n<li>Semi-Supervised Learning 5<\/li>\n<li>Parameter 5<\/li>\n<li>Population 5<\/li>\n<\/ul>\n<p><strong>Algorithms 5<\/strong><\/p>\n<ul>\n<li>Linear Regression 5<\/li>\n<li>Principal Component Analysis (PCA) 5<\/li>\n<li>K-Means 6<\/li>\n<li>Support Vector Machine (SVM) 7<\/li>\n<li>Transfer Learning 7<\/li>\n<li>Decision Tree 7<\/li>\n<li>Dimensionality Reduction 8<\/li>\n<li>Instance based learning 8<\/li>\n<li>Instance-Based Learning 8<\/li>\n<li>K Nearest Neighbors 8<\/li>\n<li>Kernel 9<\/li>\n<\/ul>\n<p><strong>Training:\u00a0Basics 9<\/strong><\/p>\n<ul>\n<li>Training 9<\/li>\n<li>Training Example 9<\/li>\n<li>Training Set 9<\/li>\n<li>Iteration 9<\/li>\n<li>Convergence 9<\/li>\n<\/ul>\n<p><strong>Training: Data 10<\/strong><\/p>\n<ul>\n<li>Standardization 10<\/li>\n<li>Holdout Set 10<\/li>\n<li>Normalization 10<\/li>\n<li>One-Hot Encoding 10<\/li>\n<li>Outlier 11<\/li>\n<li>Embedding 11<\/li>\n<\/ul>\n<p><strong>Regression 12<\/strong><\/p>\n<ul>\n<li>Regression 12<\/li>\n<li>Regression Algorithm 12<\/li>\n<li>Regression Model 12<\/li>\n<\/ul>\n<p><strong>Classification 12<\/strong><\/p>\n<ul>\n<li>Classification 12<\/li>\n<li>Class 12<\/li>\n<li>Hyperplane 12<\/li>\n<li>Decision Boundary 12<\/li>\n<li>False Negative (FN) 13<\/li>\n<li>False Positive (FP) 13<\/li>\n<li>True Negative (TN) 13<\/li>\n<li>True Positive (TP) 13<\/li>\n<li>Precision 13<\/li>\n<li>Recall 14<\/li>\n<li>F1 Score 14<\/li>\n<li>Few-Shot Learning 14<\/li>\n<li>Hinge Loss 14<\/li>\n<li>Log Loss 14<\/li>\n<\/ul>\n<p><strong>Ensemble 15<\/strong><\/p>\n<ul>\n<li>Ensemble 15<\/li>\n<li>Ensemble Learning 15<\/li>\n<li>Strong Classifier 15<\/li>\n<li>Weak Classifier 15<\/li>\n<li>Boosting 15<\/li>\n<\/ul>\n<p><strong>Evaluation 15<\/strong><\/p>\n<ul>\n<li>Validation Example 15<\/li>\n<li>Validation Loss 15<\/li>\n<li>Validation Set 16<\/li>\n<li>Variance 16<\/li>\n<li>Cost Function 16<\/li>\n<li>Cross-Validation 16<\/li>\n<li>Overfitting 16<\/li>\n<li>Regularization 16<\/li>\n<li>Underfitting 16<\/li>\n<li>Evaluation Metrics 17<\/li>\n<li>Evaluation Metric 17<\/li>\n<li>Regression metrics 17<\/li>\n<li>Mean Absolute Error. 17<\/li>\n<li>Mean Squared Error. 17<\/li>\n<li>R^2 17<\/li>\n<li>Classification metrics 17<\/li>\n<li>Accuracy. 17<\/li>\n<li>Logarithmic Loss. 17<\/li>\n<li>Area Under ROC Curve. 17<\/li>\n<li>Confusion Matrix. 17<\/li>\n<li>Hyperparameter 18<\/li>\n<li>Hyperparameter 18<\/li>\n<li>Hyperparameter Tuning 18<\/li>\n<li>Grid Search 18<\/li>\n<li>Random Search 18<\/li>\n<\/ul>\n<\/div>\n<p><a href=\"https:\/\/www.datasciencecentral.com\/xn\/detail\/6448529:BlogPost:801320\">Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Author: ajit jaokar For background to this post, please see Learn #MachineLearning Coding Basics in a weekend. Here,we present the glossary that we use for [&hellip;] <span class=\"read-more-link\"><a class=\"read-more\" href=\"https:\/\/www.aiproblog.com\/index.php\/2019\/02\/13\/learn-machinelearning-coding-basics-in-a-weekend-glossary-and-mindmap\/\">Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":469,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[26],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/1717"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=1717"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/1717\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/469"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=1717"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=1717"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=1717"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}