examples of machine learning projects

/numpy-cheat-sheet/Source: Http://datasciencefree.com/numpy.pdfSource: Https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CESource:Https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynbPandasSource:Http://datasciencefree.com/pandas.pdfSource: Https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=USource:Https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynbMatplotlibSource: Http

What is the difference between data Mining (mining), machine learning (learning), and artificial intelligence (AI)? What is the relationship between data science and business Analytics? Originally I thought there was no need to explain the problem, in the End data Mining (mining), machine

What machine learning programs have been the most watched in 2017 years. Mybridge a list of top 30 for us, with GitHub links attached to all of the following items. We compared nearly 8,800 Kaiyuan machine learning programs and selected the best of the 30. This is a very competitive list of all the outstanding

straight line, but it does not need to be guaranteed.That is, to tolerate those error points, but we have to add the penalty function so that the more reasonable the error points, the better. In fact, in many cases, the more perfect the classification function is not during training, the better, because some data in the training function is inherently noisy. It may be wrong when the classification label is manually added, if we have learned these error points during training (

will find nothing to say, just give a lot of examples. Algorithms of the Intelligent Web (Smart Web algorithm) PDF138Author Haralambos Marmanis, Dmitry Babenko. The formula in this book is a little bit more than "collective intelligence programming", the example of which is mostly the application on the Internet, to see the name. The disadvantage is that the matching code inside is BeanShell and not python or anything else. In general, this book is

Chapter Two univariate linear regression (Linear Regression with one Variable) 1.Model RepresentationIf we return to the problem of training set (Training set) as shown in the following table:The tag we will use to describe this regression problem is as follows :M represents the number of instances in the training setX represents the feature / input variableY represents the target variable / output variable(x, Y) represents an instance of a training set(x (i), Y (i)) On behalf of section I

: Network Disk DownloadToday, machine learning is making a boom on the internet, and Python is a great language for developing machine learning systems. As a dynamic language, it supports rapid exploration and experimentation, and the number of machine

instance. Examples of multi-class classification schemes include: Determine the breed of a dog as "Siberian sled Dog", "Golden Retriever", "Poodle" and so on. Interpret movie reviews as "positive", "neutral" or "negative". Classify hotel reviews as "location", "Price", "cleanliness" and so on. For more information, see the Multi-category articles on Wikipedia.Classification Step settings: Define the problem first Yo

attention.Deep Learning (learning) is a new field in ML research that is introduced into ML to bring ml closer to its original target: AI. View a brief introduction to machine learning for AI and an introduction to deep learning algorithms.Deep

=sigmoid (Z2); A2=[ones (1,size (a2,2)); A2]; Z3=THETA2*A2; A3=sigmoid (Z3); Delta_3=a3-y_vec; Gz2=[0;sigmoidgradient (z2)]; Delta_2=theta2 ' *delta_3.*gz2; Delta_2=delta_2 (2:end); Delta2=delta2+delta_3*a2 '; Delta1=delta1+delta_2*a1 '; endtheta1_grad=1/m*delta1; THETA2_GRAD=1/M*DELTA2; Theta1 (:, 1) = 0; Theta1_grad=theta1_grad+lambda/m*theta1; THETA2 (:, 1) = 0; theta2_grad=theta2_grad+lambda/m*theta2;%-------------------------------------------------------------% ====