Programming for Beginners Python 3 and The Complete Guide to Machine Learning and Python Data Science in a Week Book Free Full 2022

 Programming for Beginners 3 Manuscripts The Complete Guide to Learning Python Crash Course Python Machine Learning and Python Data Science in a Week.

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Introduction:

According to Arthur Samuel (1959); Machine Learning (or machine learning) is a field of study that allows computers to learn without being explicitly programmed. According to Tom Mitchell (1998); A computer program is said to learn from experience E with respect to a certain task T and a certain performance measure P, if T IS performance , P measured , with experience E. 

There are several types of automatic learning, including: 

- Learning (supervised, unsupervised, and reinforcement).

- Referral systems, etc.

Supervised learning: 

Supervised learning is a machine learning task of learning a predictive function from labelled examples. In other words, supervised learning is finding, from a data set En={(x1,y1),… ,(xn,yn)} a function f:X Y so that for everything (xi,yi) In we have f(xi) yi. 

 Workflow of supervised learning: EpochFail – CC BY-SA Two types of supervised learning : regression models and classification models. 

 Regression: A regression model predicts a continuous (quantitative) value. 

This means that the set of output values Y that we try to estimate with function f is a set of actuals: Y R. Suppose we want to create an f:X Y model that predicts the price of a house knowing the area in m2 In this example, X represents all the surfaces of the houses and Y represents all the prices. 

If we want to estimate the price of a house of surface s, we calculate f(s). Classification: A classification model predicts a discrete (qualitative) value.

This means that the set of Y output values that we try to estimate with function f is a finite set: Y={0,1,… ,n}.

Suppose we want to create an f:X Y model that predicts whether a mail is spam or not. In This example, X represents all the mails to be analyzed and Y={0,1}; 1 if the mail is spam and 0 otherwise. 

If we want to analyze a mail m, we calculate f(m).

Unsupervised learning:

According to wikipedia, unlike supervised learning, an unsupervised learning algorithm must operate from unlabeled examples. they must automatically extract the categories to be associated with the data (examples) submitted to them. The algorithm seeks to maximize the homogeneity of the data within each category on the one hand and, on the other hand, to form as distinct categories as possible. The most common unsupervised learning problem is clustering, which consists of grouping a set of heterogeneous elements into homogeneous sub-groups. But there is also the dimension reduction which (as its name suggests) consists in reducing the size of the data. Clustering: As indicated in my article entitled Clustering with the DBSCAN algorithm, clustering aims to divide a data set into different homogeneous “packets” in that the data of each subset share common characteristics, which most often correspond to criteria of proximity (computer similarity) which are defined by introducing measures and classes of distance between objects. Reduction of dimension: Dimension reduction is taking data from a large space, and replacing it with data from a smaller space without losing the variance. In other words, dimension reduction allows data from a large space to be projected into a smaller space. Dimension reduction is useful in machine learning because smaller data can be processed faster. And it also makes it possible to fight against the scourge of dimension. Conclusion: In this article, we have covered small introductions about the learning methods (supervised and unsupervised) most used in machine learning. These methods will be further developed on the next articles of this suite. There are other learning methods, which will also be developed in this suite of articles, such as SVM, recommendation systems, anomaly detection systems, etc.

1. Introduction to Data Science and Data

What is data science and data, the role of data scientist in today’s world, machine learning vs data science vs data analysis, the salary range for the data scientist and the future of data science? what are you going to learn – As a future data scientist, you should have a thorough knowledge of all these basic concepts. And this section will give you an overview of all the buzzwords of data science and the data science project workflow with the roles of Data Scientist, Machine Learning Engineer and Data Analyst. 2. Probability and Statistics What is a probability, the importance of probability in data science, various probability distributions and statistical concepts such as mean, mode, median and standard deviation. what are you going to learn – Probabilities and statistics help bring logic to a world filled with chance and uncertainty. This section will teach you the concepts and techniques needed to understand and explore data that can be used in various fields such as data science, engineering and finance. You will learn not only how to solve difficult technical problems, but also how to apply these solutions in everyday life.

3. Python Programming:

What is programming, introduction to the jupyter notebook, collections, keywords and variables, control flow instructions and python functions. what are you going to learn – Python is a very simple language to learn and is the best language for data science and machine learning because of extremely powerful libraries. You will learn the basics of python programming and various libraries like pandas, numpy And matplotlib. 4. Data collection and clean-up: Reading data from a local file, CSV and Excel file, reading a JSON file, reading data from an API, detecting missing data, processing missing data. what are you going to learn The most important task of the Data Scientist is to collect data from different sources and to clean and prepare this data for analysis. You will learn how to collect data from local files, CSV and Excel, JSON file and API. But when you collect it, it will definitely be in a messy format. You will also learn how to clean this data in a simple way to spend less time cleaning the data and more time exploring and modeling the data.

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