Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 Book Full free 2022

 Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 Book Full free PDF 2022.

The different machine learning techniques with Python:

There are different algorithms, techniques and methods of ML that can be used to build models in order to solve real-life problems using data. In this section, we will discuss these different types of methods. 

Supervised learning: This learning method or algorithm takes the sample of data, that is, the learning data, and the output, that is, the labels or responses, associated with each sample of data during the learning process. 

The main purpose of supervised learning algorithms is to learn an association between input data samples and corresponding outputs after performing multiple instances of training data. K-Nearest Neighbors (K nearest neighbours) SVM Kernel Naïve Bayes (Bayesian networks) Classification by decision tree Random Forest Classification The KNN (K-Nearest Neighbors) algorithm is a simple, non-parametric supervised machine learning algorithm that can be used to solve classification and regression problems. 

We will now introduce you to the project of detecting fruit from several characteristics. Based on the value of the mass, width, height and color score, we will determine if a fruit is an apple, tangerine, orange or lemon. For example, we have x as input variable and y as output variable.

The objective of a supervised learning algorithm is to find an f function for matching the input variable (x) with the output variable (Y), that is, an expression of the type Y=f(x). In order to obtain new input data (x), we can easily predict the output variable (Y) for these new input data.

The functioning of supervised learning can be easily understood through the example and diagram below:

supervised learning: Based on the tasks to be performed, supervised learning algorithms can be divided into two broad categories: The Classification And the Regression Classification: The main purpose of classification-based tasks is to predict categorical output labels or responses for the given input data. The output will be based on what the model learned during the training phase. Since we know that output categorical responses mean discrete and unordered values, each output response will belong to a specific class or category. The classification and associated algorithms will be discussed in detail in the following sections. Regression: The main purpose of regression algorithms is to predict output labels or responses that are continuous numerical values, for the given input data. The output will be based on what the model learned in its training phase. Basically, regression models use input data characteristics (independent variables) and their corresponding continuous numerical output values (outcome or dependent variables) to learn a specific association between the corresponding inputs and outputs. Unsupervised learning: The purpose of unsupervised learning is to find the underlying structure of a data set, to group that data according to its similarities, and to represent that data set in a compressed format. The operation of unsupervised learning can be understood by the diagram below: non-supervised learning: Here, we have taken unlabelled input data, which means that it is not categorized and the corresponding output is not given either. Now, these unlabeled inputs are sent to the machine learning model to train it. First, it will interpret the raw data to find hidden models of the data and then apply appropriate algorithms such as k-means clustering, decision tree, etc. After applying the appropriate algorithm, the algorithm divides the data into groups based on similarities and differences between the data. Based on the tasks to be performed, unsupervised learning algorithms can be divided into two broad categories: The clustering: Clustering is a method of grouping objects into clusters so that objects with the most similarities remain in a group. Objects in one group must have less, if any, similarity to objects in another group. Cluster analysis finds commonalities between the data and categorizes them according to the presence or absence of these commonalities. The association: An association rule is an unsupervised learning method that is used to find relationships between variables in a large database. It determines which elements appear together in a database. The association rule makes the marketing strategy more effective. For example, people who buy an item X (such as a loaf of bread) also tend to buy an item Y (butter/jam). A typical example of an association rule is the analysis of the household basket. We are now going to put machine learning into practice in Python. But before we tackle it, we invite you to download this book on the initiation to the Hadoop ecosystem.

Implementing a machine learning project with Python:

In this part of the article, we will see how to carry out a machine learning project step by step. We will study in particular the method of classification of supervised learning. For a better understanding of this tutorial you must have a basic knowledge of the python programming language and a global knowledge of the Scikit-Learn library. Our articles on Python programming for data and on Scikit-Learn will give you all the necessary basics. What is machine learning classification with Python? The classification algorithm is a supervised learning technique that is used to identify the category of new observations on the learning database. In classification, a program learns from a given set of data or observations and then classifies new observations into a number of classes or groups. For example, Yes or No, 0 or 1, Spam or No Spam, cat or dog, etc. As the classification algorithm is a supervised learning technique, it takes labeled input data, which means it contains inputs with the corresponding output. The best example of an ML classification algorithm is the email spam detector. two main categories in Classification algorithms: Linear models: Logistic regression Vector Machines Support – SVM Non-linear models: K-Nearest Neighbors (K nearest neighbours) SVM Kernel Naïve Bayes (Bayesian networks) Classification by decision tree Random Forest Classification In this part, we will try to solve a Python machine learning problem with the K-Nearest Neighbors model (K closest neighbors). The KNN (K-Nearest Neighbors) algorithm is a simple, non-parametric supervised machine learning algorithm that can be used to solve classification and regression problems. K-Nearest Neighbors (K nearest neighbours) SVM Kernel Naïve Bayes (Bayesian networks) Classification by decision tree Random Forest Classification In this part, we will try to solve a Python machine learning problem with the K-Nearest Neighbors model (K closest neighbors).

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