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BigData News Thursday, March 29 Machine learning, Machine learning project, Predictive modeling & more…

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BigData News TLDR / Table of Contents

  • Soccer and Machine Learning: 2 hot topics for 2018
    • How good is a certain soccer player? Let’s find out applying Machine Learning to Fifa 18!I’m sure you’ve probably heard about the 2018 FIFA Football World Cup…
    • machine learning, Machine Learning Project, FIFA Football World, Aman Srivastava, Github Project
  • A Tour of The Top 10 Algorithms for Machine Learning Newbies
    • predictive modeling, linear regression, logistic regression, machine learning, data
  • Artificial Neural Networks: Part1
    • Last week, I gave a one-hour seminar covering one of the machine learning tools which I have used extensively in my research: neural networks. Preparation of t…
    • function, neural network, loss function, activation function, machine learning

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Soccer and Machine Learning: 2 hot topics for 2018

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  • Although Im not a fan of video games, when I saw this dataset collected byAman Srivastava,I immediately thought that it was great for practicing some of the basics of any Machine Learning Project.
  • In thisGithub Project,you can access the CSV files that compose the dataset and some Jupyter notebooks with the python code used to collect the data.
  • Among other things, we learned that a typical workflow for a Machine Learning Project usually looks like the one shown in the image below: – – In this post well go through a simplified view of this whole process, with a practical implementation of each phase.
  • Generally any machine learning project has an initial stage known asdata prepapration, data cleaning or the preprocessing phase.
  • In hisGithub Projectyou can access some of the jupyter notebooks with the python code that acts as the data preprocessing modules that were applied to get and generate the original dataset for our project.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: machine learning, Machine Learning Project, FIFA Football World, Aman Srivastava, Github Project[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Soccer and Machine Learning: 2 hot topics for 2018 – Data Science Central[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”A-Tour-of-The-Top-10-Algorithms-for-Machine-Learning-Newbies”][vc_column width=”1/2″][vc_separator][vc_column_text]

A Tour of The Top 10 Algorithms for Machine Learning Newbies

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  • For machine learning newbies who are eager to understand the basic of machine learning, here is a quick tour on the top 10 machine learning algorithms used by data scientists.
  • For machine learning newbies who are eager to understand the basic of machine learning, here is a quick tour on the top 10 machine learning algorithms used by data scientists.
  • The representation of linear regression is an equation that describes a line that best fits the relationship between the input variables (x) and the output variables (y), by finding specific weightings for the input variables called coefficients (B).
  • We will predict y given the input x and the goal of the linear regression learning algorithm is to find the values for the coefficients B0 and B1.
  • Because of the way that the model is learned, the predictions made by logistic regression can also be used as the probability of a given data instance belonging to class 0 or class 1.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: predictive modeling, linear regression, logistic regression, machine learning, data[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]A Tour of The Top 10 Algorithms for Machine Learning Newbies[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Artificial-Neural-Networks-Part1″][vc_column width=”1/2″][vc_separator][vc_column_text]

Artificial Neural Networks: Part1

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  • From the picture above, we can see that a neural network consists of three components: the inputs, features in the data; the network, layers of neurons; and the output, the prediction of the trained model.
  • An example might be the (in)famous sigmoid function: – – The network is then many layers of neurons stacked together and connected up; the outputs of one layer are used as inputs to the next.
  • Since the activation function of each neuron is (normally) the same, this complex function must be built by adjusting the weights each neuron applies to its inputs.
  • Essentially it measures the difference between the networks predictions for a set of training data, and what the outputs should have been (the true values).
  • Now that we have a way of quantifying the current performance of our network, training it simply becomes an optimization problem of minimizing the loss function.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: function, neural network, loss function, activation function, machine learning[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Artificial Neural Networks: Part1 – Data Science Central[/vc_column_text][/vc_column][/vc_row]