Machine Learning Introduction: A Comprehensive GuideVictor RomanBlockedUnblockFollowFollowingDec 3This is the first of a series of articles in which I will describe machine learning concepts, types, algorithms and python implementations.The main goals of this series are:Creating a comprehesive guide towards machine learning theory and intuition.Sharing and explaining machine learning projects, developed in python, to show in a practical way the concepts and algorithms explained, as well as how they can be applied in real-world problems.Leaving a digital footprint of my knowledge in the subject and inspire others to learn and apply machine learning in their own fields.The information exposed in this series comes from several sources, being the main ones:Machine Learning Engineer NanoDegree (Udacity)Python Machine Learning book (by Sebastian Raschka & Vahid Mirjalili)Deep Learning with Python book(by Francois Chollet)Machine Learning Mastery with Python book(by Jason Brownlee)Python Data Science and Machine Learning course by Jose Portilla (Udemy)Machine Learning y Data Science con Python course by Manuel Garrido (Udemy)WHAT IS MACHINE LEARNING?Due to the large decrease in technology and sensors prices, we can now create, store and send more data than ever in history..This data feed the machine learning models and it is the main driver of the boom that this science has experienced in recent years.Machine Learning is one of the subfields of Artificial Inteligence and can be described as:“Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”Machine learning offers an efficient way for capturing knowledge in data to gradually improve the performance of predictive models, and make data-driven decisions..This data is used normally in matrix form where:Each separate row of the matrix is a sample, observation or data point.Each column is feature (or attribute) of that observation.Usually there is one column (or feature), that we will call the target, label or response, and its the value or class that we’re trying to predict.To train a machine learning model is to provide a machine learning algorithm with training data to learn from it.Regarding machine learning algorithms, they usually have some inner parameters..ie: In Decision Trees, there are parameters like depth, number of nodes, number of leaves… This inner parameters are called hyperparameters.Generalization is the ability of the model to make predictions on new data.Types of machine learningThere are 3 types of machine learnign:Supervised learningUnsupervised learningReinforcement learning.In this series we will explore and study all of the metioned types and subfields of machine learning and we will also talk about deep learning, a subfield of machine learning that has grown greatly in the last years.Supervised LearningSupervised learning refers to a kind of machine learning models that are trained with a set of samples where the desired output signals(or labels) are already known..It then repeats the process, K times and computes the average performance of the model by dividing the sum of the metrics obtained between the K number of iterations.In general, the default parameters of the machine learning algorithms provided by the libraries are not the best ones to use with our data, so we will use hyperparameter optimization techniques to help us to do the fine tunning of the model’s performance.Evaluating Models and Predicting with Unseen DataOnce we have selected and fitted a model to our training dataset, we can use the testing dataset to estimate the performance on this unseen data, so we can make an estimation of the generalization error of the model..Or evaluate it using some other metric.If we are satisfied with the value of the metric obtained, we can use then the model to make predictions on future data.Wrap UpIn this article we learned what is Machine Learning painting a big picture of its nature, motivation and applications.We also learned some basic notations and terminology and the different kinds of machine learning algorithms:Supervised learning with classification and regression problems.Unsupervised learning with clustering and dimensionality reduction.Reinforcement learning, where the agent learn from its environment.Deep learning and their artificial neuron networks.Finally, we made an introduction to the typical methodology for building Machine Learning models and explained its main tasks:Preprocessing.Training and testing.Selecting a model.Evaluating.As stated on the begining of the article, this is the first of a series of articles, and was a general introduction.. More details