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    Python Machine Learning: Machine Learning Algorithms for Beginners - Data Management and Analytics for Approaching Deep Learning and Neural Networks from Scratch
    Python Machine Learning: Machine Learning Algorithms for Beginners - Data Management and Analytics for Approaching Deep Learning and Neural Networks from Scratch
    Python Machine Learning: Machine Learning Algorithms for Beginners - Data Management and Analytics for Approaching Deep Learning and Neural Networks from Scratch
    Ebook130 pages2 hours

    Python Machine Learning: Machine Learning Algorithms for Beginners - Data Management and Analytics for Approaching Deep Learning and Neural Networks from Scratch

    By Ahmed Ph. Abbasi

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    About this ebook

    How can a beginner approach machine learning with Python from scratch?
    Why exactly is machine learning such a hot topic right now in the business world?
    Ahmed Ph. Abbasi will lead you from being a complete beginner in learning a sound method of data analysis that uses algorithms, which learn from data and produce actionable and valuable information.
    The basis for understanding deep learning and neural networks will be laid, and you will be able to write simple beginner level codes using Python.

    LanguageEnglish
    PublisherPAT NAKAMOTO
    Release dateAug 4, 2018
    ISBN9781386293378
    Python Machine Learning: Machine Learning Algorithms for Beginners - Data Management and Analytics for Approaching Deep Learning and Neural Networks from Scratch

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      Book preview

      Python Machine Learning - Ahmed Ph. Abbasi

      LIST OF FIGURES

      ––––––––

      Figure ‎1.1 Mobile Network classification based on population and coverage area

      Figure ‎1.2 Second-handed car price expectation following regression

      Figure ‎2.1 splitting dataset into testing and training folds

      Figure 2.2 10-fold cross-validation example

      Figure ‎3.1 The scatter data and the linear regression equation plot

      Figure ‎3.2 Decision tree flow chart

      Figure ‎4.1 K-mean clustering algorithm, flow chart

      Figure ‎5.1 Human Neuron

      Figure ‎5.2 Neural Network layers

      Figure ‎5.3 Detailed Neural Network

      Figure ‎5.4 Neural Network, Hospitality Probability Example

      Figure ‎5.5 Non-Convex data

      LIST OF TABLES

      ––––––––

      Table ‎1.1 Common algorithms used to perform supervised and unsupervised machine learning

      Table ‎3.1 Age and Glucose relationship

      Table ‎3.2 Age and Glucose relationship

      Table ‎3.3 Summary of the Decision Tree Algorithm

      Table ‎5.1 Activation Functions

      Table ‎5.2 Comparison between Gradient Decent and Stochastic Gradient Decent

      Chapter One

      Introduction

      1.1 What is Machine Learning

      I still remember a story from my first year in primary school named: Operation Mastermind[1]. In that story, a master computer controls all the systems in the island. Finally, he decides to get rid of human control and to seize all power himself. He begins to manage the island and its systems depending on his own decisions! Although the current situation of machine is still far of this to happen, people believe that science fiction always comes true!

      Human power is normally limited. The heaviest weight ever lifted by a human being was 6,270 Ib (2,840 Kg)[2]. That was a great record compared to the average human power. However, it is nothing when compared to the power of machines, which had been invented by human himself to lift tens of tons of kilograms. This is a simple analogy to the realization of machine learning power and its capabilities. To imagine the situation, it is known that the data analysis and processing capabilities of a well-trained human is limited in terms of the amount of data being processed, time consumption and also the probability of making errors. On the other hand, the machines/computers designed, built and programmed by human can process a massive amount of data in much less time than human with almost no errors. Besides, electronic machine never takes a break and never let its own opinion affect its analyzing process and results.

      To grasp the concept of machine learning, take a corporate or a governmental distributed building for example, in which seeking the optimal energy consumption is the main goal. The building consists of walls, floors, doors, windows, furniture, a roof, etc., which are the general architecture elements of a building. These elements consist normally of different kinds of materials and show different reactions to energy, daylight absorption and reflection. Also, the building encounters different amount of sun radiation, sun positions, wind and weather conditions that varies on hourly basis. Now, consider that the Energy and Electrical Engineers have decided to construct a photovoltaic system on the building. The optimal design in this case would be when they consider the previous aspects, beside those that are related to choosing the optimal places, orientation, shadowing and angles, considering the directions of the sun on hourly basis for the whole year. Last but not least, the building energy requirements for heating, cooling, lighting, etc. has to be clearly estimated. This is a complex and a massive amount of data considering that this is collected on hourly basis, as mentioned above. What the corporation aspire to achieve is predicting the optimal model of their building design that maximizes the renewable power production and minimizes the energy consumption. This kind of data changes according to changes in time and geographic location, which makes the job very hard for classical ways of programming. Machine learning, on the other hand, is the solution when it is related to variable and large amount of data. The main goal of machine learning is to develop a suitable and optimal algorithm that leads to the best decisions.

      1.2 Machine Learning and Classical Programming

      It is very common to know a programmer who implements an algorithm via a programming language. The programmer gives the chip/machine specific program commands, containing the input parameters and the expected kind of outputs. The program then runs and processes the data, while being restricted by the code entered by the programmer. This kind of programming does not contain the realization of Learning which means the ability to develop solution based on background examples, experience or statics. A machine equipped with a learning algorithm is able to take different decisions that is suitable for every situation.  

      Practically, in machine learning, the computer concludes automatically to an algorithm that can process the dataset to produce the desired output. whereas, the concept is different in classical machine programming. Take, for example, the sorting algorithm. We have already many sorting algorithms that can deal with our inputs and give us a sorted output. Our mission here is just to choose the best sorting algorithm that can do the work efficiently. On the other hand, in machine learning, there exists many applications in which we do not have classical algorithms that are totally ready to give us the desired output. Instead, we have what is called: example data. In the machine learning era, no instructions

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