Machine Learning Part 1 Introduction Welcome Artificial Intelligence

Machine Learning Part 1. Introduction Welcome Artificial Intelligence DR. GHASSAN ISSA 2020

Machine Learning

Def. of ML "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. " – Tom M. Mitchell “ Machine Learning is the ability of a computer Program to enhance both its performance and Competence with Repeated Use. ” - Our definition

Phases of ML Phase 1—Training Phase: This is the phase where training data is used to train the model by pairing the given input with the expected output. The output of this phase is the learning model itself. Phase 2—Validation and Test Phase: This phase is to measure how good the learning model that has been trained is and estimate the model properties, such as error measures, recall, precision, and others. This phase uses a validation dataset, and the output is a sophisticated learning model. Phase 3—Application Phase: In this phase, the model is subject to the real-world data for which the results need to be derived.

Machine Learning can be classified into several categories or types as follows: Whether or not they are trained with human supervision: o supervised, o unsupervised, o semisupervised, and o Reinforcement Learning Whether or not they can learn incrementally on the fly (online versus batch learning) Whether they work by simply comparing new data points to known data points, or instead detect patterns in the training data and build a predictive model, much like scientists do (instance-based versus model-based learning)

Algorithm k-Nearest Neighbors Linear Regression Logistic Regression Support Vector Machines (SVMs) Decision Trees and Random Forests Neural networks Clustering k-Means Hierarchical Cluster Analysis (HCA) Expectation Maximization Principal Component Analysis (PCA) Kernel PCA Association rule learning (Apriori) Association rule learning (Eclat) Type supervised supervised Unsupervised unsupervised unsupervised

Machine Learning Frameworks and Libraries Tensor. Flow Torch Julia (language) Caffe Shell (language) Theano R (Language) Amazon Machine Learning Type. Script Accord. Net Scala Scikit-learn Apache Mahout Microsoft Cognitive Toolkit Keras

Tensor. Flow is an open-source, Java. Script library and one of the best machine learning frameworks of 2020. It is a free platform with APIs that help in building and training the ML models. Developed by Google, Tensor. Flow is the best machine learning tool that offers extensive, flexible features, a vast programming library, and resources for an array of development tasks. It also supports classifications, regressions, and neural networks, including writing algorithms for software.

Google Cloud ML Engine is one of the best Deep Learning frameworks that offer training for amateurs, among other services for building machine learning models. It helps developers and data scientists with forecasting in various fields and domains.

Apache Mahout is one Deep Learning platform operating on a distributed linear algebra framework to scribe and implement ML algorithms. The scalable algorithms are for classification, clustering, and batch-based collaborative filtering. Developed by Apache Software Foundation, it is also an open-source, free platform that uses the Map. Reduce paradigm and runs on top of Apache Hadoop. Furthermore, as one of the best machine learning tools, Mahout offers matrix and vector libraries, distributed fitness functions for programming, and more.

Shogun is another open-source machine learning framework compatible with the C++ programming language. It is a free platform that developers can use to design algorithms and data structures, primarily for ML problems in education and research. Gunnar Raetsch and Soeren Sonnenburg designed Shogun in 1999 to support vector machines for classifications and regression problems, plus large-scale learning. It allows developers to connect with other machine learning libraries, including Lib. Linear, Lib. SVM, SVMLight, Lib. OCAS, and more.

Sci-Kit Learn is a machine learning framework that supports development in Python with a library for Python programming language. It is one of the best ML frameworks for data mining and data analysis. Sci. Kit Learn also supports designing models and algorithms for classifications, regression, clustering, pre-processing, Dimensional reduction, and Model selection.

Microsoft Cognitive Toolkit (CNTK) A Machine Learning platform from Microsoft, CNTK, describes neural networks as a sequence of computational steps through directed graphs. It is also an open-source framework designed with algorithms in C++ programming language and production readers. CNTK is best when dealing with large-scale, multi-dimensional, or sparse data sets from C++, Python, and Brain. Script. It helps developers in merging various ML model types, including recurrent networks, feed-forward deep neural networks, and convolutional neural networks.