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(PDF) Hands on Machine Learning with Scikit Learn and Tensorflow | jack house - blogger.com
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron Scikit-Learn, Keras, and TensorFlow, the cover image, Download the Data 49 Take a TensorFlow was created at Google and supports many of their large-scale Machine Learning applications. It was open-sourced in Novem‐ ber The book favors a hands-on approach, growing an intuitive understanding of Machine Learning through concrete working examples and Hands on machine learning with scikit-learn and tensorflow pdf github This repo is home to notes & code that accompanies Part 1 of Aurelien Geron's "Hands-on ML with Scikit-Learn, Keras & TF" book. The book provides a comprehensive overview of data science, machine learning (with scikit-learn), and deep learning (with tensorflow)
Hands-on machine learning with scikit-learn and tensorflow pdf download
It is crucial that your training data be representative of the new cases you want to generalize to. Training data full of errors, outliers and noise e. Overfitting due to model being too complex relative to the amount and noise of the training data. Split your data into two sets: training and test. The error rate is called generalization error out-of-sample errorby evaluating your model on the test set, you get an estimate of this error.
Holdout validation : hold out part of the training set to evaluate several candidate models and select the best one. The held-out set is called validation set or development setor dev set. After the validation process, you train the best model on the full training set including the validation setand this gives you the final model, hands-on machine learning with scikit-learn and tensorflow pdf download.
Lastly, you evaluate this final model on the test set to get an estimate of the generalization error. If the validation set is too large, the remaining training set will be much smaller than the full training set. Solution: perform repeated cross-validationusing many validation sets. Each model is evaluated once per validation set after it is trained on the rest of the data. By averaging out all the evaluations of a model, you have a more accurate measure of its performance but… more training time.
The validation set and test set must be as representative as possible of the data you expect to use in production. If after the model trained on the training set performs well on the train-devthen the model is not overfitting.
Conversely, if the model performs poorly on the train-dev set, it must have overfitted the training set. NO FREE LUNCH THEOREM : If you make absolutely no assumption about the data, then there is no reason to prefer one model over any other.
There is no model that is a priori guaranteed to work better A model is a simplified version of the observations. The simplifications are meant to discard the superfluous details that are unlikely to generalize to new instances.
Each component is fairly self-contained. WARNING : Correlation coefficient only measures linear correlation, it may completely miss out on non-linear relationships!
After engineering features, you may want to look at the correlations again to check if the features created are more correlated with the target. This is an iterative process: get a prototype up and running, analyze its output, come back to this exploration step. Instead of doing this manually, you should write functions for this purpose: reproductibility, reuse in your live system, quickly try various transformations to see which combination works best. WARNING : if you hands-on machine learning with scikit-learn and tensorflow pdf download to fill the missing values, save the value you computed to fill the training set.
You will need it later to replace missing values in the test set. impute import SimpleImputer. You may want to replace the categorical input with useful numerical features related to it. Try out many other models from various categories, without spending too much time tweaking the hyperparameters.
The goal is to shortlist a few two to five promising models. When the hyperparameter search space is large, it is often preferable to use RandomizedSearchCV. Another way to fine-tune your system is to try to combine the models that perform best.
The group ensemble will often perform better than the best individual model. If you did a lot of hyperparameter tuning, the performance may be worse than your cv slightly overfit to hands-on machine learning with scikit-learn and tensorflow pdf download training set.
Resist the temptation to tweak the hyperparameters to make the numbers look good on the test set. One way to do this is to save the trained Scikit-Learn model e. But deployment is not the end of the story. If the data keeps evolving, you will need to update your datasets and retrain your model regularly. You should probably automate the whole process as much as possible. Here are a few things you can automate:. Some learning algorithms are sensitive to the order of the training instances, and they perform poorly if they get many similar instances in a row.
SGDClassifier on sklearn. Has the advantage of being capable of handling very large datasets efficiently. Deals with training instances independently, one at a time suited for online learning. High accuracy can be deceiving if you are dealing with skewed datasets i. Each row represents an actual classwhile each column represents a predicted class. Harmonic mean of the precision and recall. The harmonic mean gives much more weight to low values, so the classifier will only get a high F1 score if both recall and precision are high.
Scikit-Learn gives you access to the decision scores that it uses to make predicitions. For RandomForestClassifier for example, the method to use is. Receiver operating characteristic ROC curve. Plots the true positive rate recall against the false positive rate FPR. The FPR is the ratio of negative instances that are incorrectly classified as positive. It is equal to hands-on machine learning with scikit-learn and tensorflow pdf download - true negative rate TNR, or specificity which is the ratio of negative instances that are correctly classified as negative.
The higher the recall TPRthe more false positives FPR the classifier produces. The purely random classifier is the diagonal line in the plot, a good classifier stays as far away from that line as possible toward the top-left corner. A perfect classifier will have a ROC AUC equal to 1, whereas a purely random classifier will have a ROC AUC equal to 0.
You should prefer the PR curve whenever the positive class is rare or when you care more about the false positives than the false negatives. Otherwise, use the ROC curve. Some algorithms are not capable of handling multiple classes natively e. For 10 classes you would train 10 binary classifiers and select the class whose classifier outputs the highest score. This is the one-versus-the-rest OvR strategy also called one-versus-all. One-versus-one OvO strategy: trains a binary classifier for every pair of digits.
Good strategy for SVM that scales poorly with the size of the training set. Scikit-Learn detects when you try to use a binary classification algorithm for a multiclass classification task, and it automatically runs OvR or OvO, depending on the algorithm.
Outputs multiple binary tags e. Evaluate a multilabel classifier: One approach is to measure the F1 score for each individual label, then simply compute the average score. Generalization of multilabel classification where each label can be multiclass i. A linear model makes a prediction by simply computing a weighted sum of the input features, plus a constant called the bias term also called the intercept term.
Generic optimization algorithm capable of finding optimal solutions to a wide range of problems. The general idea of Gradient Descent is to tweak parameters iteratively in order to minimize a cost function. It measures the local gradient of the error function with regard to the parameter vector θ, and it goes in the direction of descending gradient.
Once the gradient is zero, you have reached a minimum! The size of the steps, is determined by the learning rate hyperparameter. If the learning rate is too small, then the algorithm will have to go through many iterations to converge, which will take a long time.
If the learning rate is too high, you might jump across the valley and end up on the other side, possibly even higher up than you were before. This might make the algorithm diverge, with larger and larger values, failing to find a good solution. The MSE cost function for a Linear Regression is a convex function : if you pick any two points on the curve, the line segment joining them never crosses the curve.
This implies that there are no local minima, just one global minimum. It is also a continuous function with a slope that never changes abruptly. Consequence: Gradient Descent is guaranteed to approach arbitrarily close the global minimum if you wait long enough and if the learning rate is not too high.
When using Gradient Descent, you should ensure that all features have a similar scale, or else it will take much longer to converge, hands-on machine learning with scikit-learn and tensorflow pdf download.
Possible to train on huge training sets, since only one instance in memory at each iteration. If your model is underfitting the training data, adding more training examples will not help. You need to use a more complex model or come up with better features. If there is a gap between the curves. This means that the model performs significantly better on the training data than on the validation data, which is the hallmark of an overfitting model. This is why it is called a trade-off. Keep the models weights as small as possible, hands-on machine learning with scikit-learn and tensorflow pdf download.
It is important to scale the data before performing Ridge Regression, as it is sensitive to the scale of the input features. This is true of most regularized models. Note that the regularization term should only be added to the cost function during training. It is quite common for the cost function used during training to be different from the performance measure used for testing. Apart from regularization, another reason they might be different is that a good training cost function should have optimization-friendly derivatives, while the performance measure used for testing should be as close as possible to the final objective.
Least Absolute Shrinkage and Selection Operator Regression, hands-on machine learning with scikit-learn and tensorflow pdf download. Tends to eliminate the weights of the least important features. When should you use plain Linear Regression i. It is almost always preferable to have at least a little bit of regularization, so generally you should avoid plain Linear Regression. In general, Elastic Net is preferred over Lasso because Lasso may behave erratically when the hands-on machine learning with scikit-learn and tensorflow pdf download of features is greater than the number of training instances or when several features are strongly correlated.
Introduction to Machine Learning with Scikit-Learn (Spring 2021)
, time: 3:28:53Hands-on machine learning with scikit-learn and tensorflow pdf download
Hands on machine learning with scikit-learn and tensorflow 2nd edition pdf download Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Second Edition (4-Colour Edition) Hands on machine learning with scikit-learn and tensorflow pdf github This repo is home to notes & code that accompanies Part 1 of Aurelien Geron's "Hands-on ML with Scikit-Learn, Keras & TF" book. The book provides a comprehensive overview of data science, machine learning (with scikit-learn), and deep learning (with tensorflow) 30/10/ · Download the Book:Hands-On Machine Learning With Scikit-Learn And Tensorflow: Concepts Tools And Techniques To Build Intelligent Systems PDF For Free, Pr
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