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Machine Learning Cheat Sheets

In this cheat sheet, you'll have a guide around the top machine learning algorithms, their advantages and disadvantages, and use-cases. 

Machine Learning Cheat Sheet

<p>&nbsp;</p>
<p>&nbsp;</p>
<p><a href="http://keras.io/">Keras</a> is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.</p>
<p>We recently launched one of the first online interactive deep learning course using Keras 2.0, called "<a href="https://www.datacamp.com/courses/deep-learning-in-python/">Deep Learning in Python</a>".</p>
<p>Now, DataCamp has created a Keras cheat sheet for those who have already taken the course and that still want a handy one-page reference or for those who need an extra push to get started.</p>
<p>In no time, this Keras cheat sheet will make you familiar with how you can load datasets from the library itself, preprocess the data, build up a model architecture, and compile, train, and evaluate it. As there is a considerable amount of freedom in how you build up your models, you'll see that the cheat sheet uses some of the simple&nbsp;key&nbsp;code examples of the Keras library that you need to know to get started with building your own neural networks in Python.</p>
<p>Furthermore, you'll also see some examples of how to inspect your model, and how you can save and reload it. Lastly, you&rsquo;ll also find examples of how you can predict values for test data and how you can fine tune your models by adjusting the optimization parameters and early stopping.</p>
<p>In short, you'll see that this cheat sheet not only presents you with the six steps that you can go through to make neural networks in Python with the Keras library.</p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/af9bb467-170d-41c9-a0bd-26e675384c4e" target="_blank" rel="noopener"><img style="width: 800px; height: 551px;" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839502/Keras_Cheat_Sheet-_Neural_Networks_in_Python_tjhi1q.png" alt="keras cheat sheet" /></a></p>
<p>In short, this cheat sheat will boost your journey with deep learning in Python: you'll have preprocessed, created, validated and tuned your deep learning models in no time thanks to the code examples! &nbsp; &nbsp;</p>
<p><strong>(Click above to download a printable version or read the online version below.) </strong></p>
<div id="scoped-content">
<h2>Python For Data Science Cheat Sheet: Keras</h2>
<p><code>Keras is a powerful and easy-to-use deep learning library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.</code></p>
<h3>A Basic Example</h3>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; from keras.models import Sequential
&gt;&gt;&gt; from keras.layers import Dense
&gt;&gt;&gt; data = np.random.random((1000,100))
&gt;&gt;&gt; labels = np.random.randint(2,size=(1000,1))
&gt;&gt;&gt; model = Sequential()
&gt;&gt;&gt; model.add(Dense(32, activation='relu', input_dim=100))
&gt;&gt;&gt; model.add(Dense(1, activation='sigmoid'))
&gt;&gt;&gt; model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
&gt;&gt;&gt;  model.fit(data,labels,epochs=10,batch_size=32)
&gt;&gt;&gt;  predictions = model.predict(data)</code></pre>
<h3>Data</h3>
<p>Your data needs to be stored as NumPy arrays or as a list of NumPy arrays. Ideally, you split the data in training and test sets, for which you can also resort to the <code class="  language-python">train_test_split</code> module of <code class="  language-python">sklearn.cross_validation</code>.</p>
<h4>Keras Data Sets</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.datasets import boston_housing, mnist, cifar10, imdb
&gt;&gt;&gt; (x_train,y_train),(x_test,y_test) = mnist.load_data()
&gt;&gt;&gt; (x_train2,y_train2),(x_test2,y_test2) = boston_housing.load_data()
&gt;&gt;&gt; (x_train3,y_train3),(x_test3,y_test3) = cifar10.load_data()
&gt;&gt;&gt; (x_train4,y_train4),(x_test4,y_test4) = imdb.load_data(num_words=20000)
&gt;&gt;&gt; num_classes = 10</code></pre>
<h4>Other</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from urllib.request import urlopen
&gt;&gt;&gt; data = np.loadtxt(urlopen("http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"),delimiter=",")
&gt;&gt;&gt; X = data[:,0:8]
&gt;&gt;&gt; y = data [:,8]</code></pre>
<h3>Preprocessing</h3>
<h4>Sequence Padding</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.preprocessing import sequence
&gt;&gt;&gt; x_train4 = sequence.pad_sequences(x_train4,maxlen=80)
&gt;&gt;&gt; x_test4 = sequence.pad_sequences(x_test4,maxlen=80)</code></pre>
<h4>One-Hot Encoding</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.utils import to_categorical
&gt;&gt;&gt; Y_train = to_categorical(y_train, num_classes)
&gt;&gt;&gt; Y_test = to_categorical(y_test, num_classes)
&gt;&gt;&gt; Y_train3 = to_categorical(y_train3, num_classes)
&gt;&gt;&gt; Y_test3 = to_categorical(y_test3, num_classes)</code></pre>
<h4>Train And Test Sets</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from sklearn.model_selection import train_test_split
&gt;&gt;&gt; X_train5, X_test5, y_train5, y_test5 = train_test_split(X, y, test_size=0.33, random_state=42)</code></pre>
<h3>Standardization/Normalization</h3>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from sklearn.preprocessing import StandardScaler
&gt;&gt;&gt; scaler = StandardScaler().fit(x_train2)
&gt;&gt;&gt; standardized_X = scaler.transform(x_train2)
&gt;&gt;&gt; standardized_X_test = scaler.transform(x_test2)</code></pre>
<h3>Model Architecture</h3>
<h4>Sequential Model</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.models import Sequential
&gt;&gt;&gt; model = Sequential()
&gt;&gt;&gt; model2 = Sequential()
&gt;&gt;&gt; model3 = Sequential()</code></pre>
<h4>Multi-Layer Perceptron (MLP)</h4>
<p><strong>Binary Classification</strong></p>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.layers import Dense
&gt;&gt;&gt; model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))
&gt;&gt;&gt; model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
&gt;&gt;&gt; model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))</code></pre>
<p><strong>Multi-Class Classification</strong></p>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.layers import Dropout
&gt;&gt;&gt; model.add(Dense(512,activation='relu',input_shape=(784,)))
&gt;&gt;&gt; model.add(Dropout(0.2))
&gt;&gt;&gt; model.add(Dense(512,activation='relu'))
&gt;&gt;&gt; model.add(Dropout(0.2))
&gt;&gt;&gt; model.add(Dense(10,activation='softmax'))</code></pre>
<p><strong>Regression</strong></p>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model.add(Dense(64, activation='relu', input_dim=train_data.shape[1]))
&gt;&gt;&gt; model.add(Dense(1))</code></pre>
<h4>Convolutional Neural Network (CNN)</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.layers import Activation, Conv2D, MaxPooling2D, Flatten
&gt;&gt;&gt; model2.add(Conv2D(32, (3,3), padding='same', input_shape=x_train.shape[1:]))
&gt;&gt;&gt; model2.add(Activation('relu'))
&gt;&gt;&gt; model2.add(Conv2D(32, (3,3)))
&gt;&gt;&gt; model2.add(Activation('relu'))
&gt;&gt;&gt; model2.add(MaxPooling2D(pool_size=(2,2)))
&gt;&gt;&gt; model2.add(Dropout(0.25))
&gt;&gt;&gt; model2.add(Conv2D(64, (3,3), padding='same'))
&gt;&gt;&gt; model2.add(Activation('relu'))
&gt;&gt;&gt; model2.add(Conv2D(64, (3, 3)))
&gt;&gt;&gt; model2.add(Activation('relu'))
&gt;&gt;&gt; model2.add(MaxPooling2D(pool_size=(2,2)))
&gt;&gt;&gt; model2.add(Dropout(0.25))
&gt;&gt;&gt; model2.add(Flatten())
&gt;&gt;&gt; model2.add(Dense(512))
&gt;&gt;&gt; model2.add(Activation('relu'))
&gt;&gt;&gt; model2.add(Dropout(0.5))
&gt;&gt;&gt; model2.add(Dense(num_classes))
&gt;&gt;&gt; model2.add(Activation('softmax'))</code></pre>
<h4>Recurrent Neural Network (RNN)</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.klayers import Embedding,LSTM
&gt;&gt;&gt; model3.add(Embedding(20000,128))
&gt;&gt;&gt; model3.add(LSTM(128,dropout=0.2,recurrent_dropout=0.2))
&gt;&gt;&gt; model3.add(Dense(1,activation='sigmoid'))</code></pre>
<h3>Inspect Model</h3>
<p>Model output shape</p>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model.output_shape</code></pre>
<p>Model summary representation</p>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model.summary()</code></pre>
<p>Model configuration</p>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model.get_config()</code></pre>
<p>List all weight tensors in the model</p>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model.get_weights()</code></pre>
<h3>Compile Model</h3>
<h4>Multi-Layer Perceptron (MLP)</h4>
<p><strong>MLP: Binary Classification</strong></p>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])</code></pre>
<p><strong>MLP: Multi-Class Classification</strong></p>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])</code></pre>
<p><strong>MLP: Regression</strong></p>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])</code></pre>
<h4>Recurrent Neural Network (RNN)</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model3.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])</code></pre>
<h3>Model Training</h3>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model3.fit(x_train4, y_train4, batch_size=32, epochs=15, verbose=1, validation_data=(x_test4, y_test4))</code></pre>
<h3>Evaluate Your Model's Performance</h3>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; score = model3.evaluate(x_test, y_test, batch_size=32)</code></pre>
<h3>Prediction</h3>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; model3.predict(x_test4, batch_size=32)
&gt;&gt;&gt; model3.predict_classes(x_test4,batch_size=32)</code></pre>
<h3>Save/Reload Models</h3>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.models import load_model
&gt;&gt;&gt; model3.save('model_file.h5')
&gt;&gt;&gt; my_model = load_model('my_model.h5')</code></pre>
<h3>Model Fine-Tuning</h3>
<h4>Optimization Parameters</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.optimizers import RMSprop
&gt;&gt;&gt; opt = RMSprop(lr=0.0001, decay=1e-6)
&gt;&gt;&gt; model2.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])</code></pre>
<h4>Early Stopping</h4>
<pre class="language-python" tabindex="0"><code>&gt;&gt;&gt; from keras.callbacks import EarlyStopping
&gt;&gt;&gt; early_stopping_monitor = EarlyStopping(patience=2)
&gt;&gt;&gt; model3.fit(x_train4, y_train4, batch_size=32, epochs=15, validation_data=(x_test4, y_test4), callbacks=[early_stopping_monitor])</code></pre>
<h3>Going Further</h3>
<p>Begin with <a href="https://www.datacamp.com/community/tutorials/deep-learning-python">our Keras tutorial for beginners</a>, in which you'll learn in an easy, step-by-step way how to explore and preprocess the wine quality data set, build up a multi-layer perceptron for classification and regression tasks, compile, fit and evaluate the model and fine-tune the model that you have built.</p>
<p>Also, don't miss out on our<a href="https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet/"> Scikit-Learn cheat sheet</a>, <a href="https://www.datacamp.com/community/blog/python-numpy-cheat-sheet/">NumPy cheat sheet</a> and <a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas cheat sheet</a>!</p>
</div>

Make your own neural networks with this Keras cheat sheet to deep learning in Python for beginners, with code samples. 

Keras Cheat Sheet: Neural Networks in Python

<p>Most of you who are learning data science with Python will&nbsp;have definitely heard already about&nbsp;scikit-learn, the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.&nbsp;</p>
<p>If you're still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.&nbsp;</p>
<p>That's why DataCamp has created a&nbsp;scikit-learn&nbsp;cheat sheet for those of you who have already started learning about the Python package, but that still want a handy reference sheet. Or, if you still have no idea about how&nbsp;scikit-learn&nbsp;works, this machine learning cheat sheet might come in handy to get a quick first idea of the basics that you need to know to get started.&nbsp;</p>
<p>Either way, we're sure that you're going to find it useful when you're tackling machine learning problems! &nbsp;</p>
<p>This&nbsp;scikit-learn&nbsp;cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you'll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.&nbsp;</p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/eb807da5-dce5-4b97-a54d-74e89f14266b" target="_blank" rel="noopener" data-ol-has-click-handler=""><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625837937/Scikit-Learn_Cheat_Sheet-_Python_Machine_Learning_wxjgwe.png" alt="Scikit-Learn Cheat Sheet" /></a></p>
<p>In short, this cheat sheet will kickstart your data science&nbsp;projects: with the help of code examples, you'll have created, validated and tuned your machine learning models in no time. &nbsp;</p>
<p>So what are you waiting for? Time to get started!</p>
<p>(Click above to download a printable version or read the online version below.)&nbsp;</p>
<div id="scoped-content">
<h2>Python For Data Science Cheat Sheet: Scikit-learn</h2>
<p>Scikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface.</p>
<h3>A Basic Example</h3>
<pre class="language-python"><code>from sklearn import neighbors, datasets, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
iris = datasets.load_iris()
X, y = iris.data[:, :2], iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
knn = neighbors.KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_score(y_test, y_pred)</code></pre>
<h3>Loading The Data</h3>
<p>Your data needs to be numeric and stored as NumPy arrays or SciPy sparse matrices. Other types that are convertible to numeric arrays, such as Pandas DataFrame, are also acceptable.</p>
<pre class="language-python"><code>import numpy as np
X = np.random.random((10,5))
y = np.array(['M','M','F','F','M','F','M','M','F','F','F'])
X[X &lt; 0.7] = 0</code></pre>
<h3>Preprocessing The Data</h3>
<h4>Standardization</h4>
<pre class="language-python"><code>from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(X_train)
standardized_X = scaler.transform(X_train)
standardized_X_test = scaler.transform(X_test)</code></pre>
<h4>Normalization</h4>
<pre class="language-python"><code>from sklearn.preprocessing import Normalizer
scaler = Normalizer().fit(X_train)
normalized_X = scaler.transform(X_train)
normalized_X_test = scaler.transform(X_test)</code></pre>
<h4>Binarization</h4>
<pre class="language-python"><code>from sklearn.preprocessing import Binarizer
binarizer = Binarizer(threshold=0.0).fit(X)
binary_X = binarizer.transform(X)</code></pre>
<h4>Encoding Categorical Features</h4>
<pre class="language-python"><code>from sklearn.preprocessing import LabelEncoder
enc = LabelEncoder()
y = enc.fit_transform(y)</code></pre>
<h4>Imputing Missing Values</h4>
<pre class="language-python"><code>from sklearn.preprocessing import Imputer
imp = Imputer(missing_values=0, strategy='mean', axis=0)
imp.fit_transform(X_train)</code></pre>
<h4>Generating Polynomial Features</h4>
<pre class="language-python"><code>from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(5)
oly.fit_transform(X)</code></pre>
<h3>Training And Test Data</h3>
<pre class="language-python"><code>from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)</code></pre>
<h3>Create Your Model</h3>
<h4>Supervised Learning Estimators</h4>
<p>Linear Regression</p>
<pre class="language-python"><code>from sklearn.linear_model import LinearRegression
lr = LinearRegression(normalize=True)</code></pre>
<p>Support Vector Machines (SVM)</p>
<pre class="language-python"><code>from sklearn.svm import SVC
svc = SVC(kernel='linear')</code></pre>
<p>Naive Bayes</p>
<pre class="language-python"><code>from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()</code></pre>
<p>KNN</p>
<pre class="language-python"><code>from sklearn import neighbors
knn = neighbors.KNeighborsClassifier(n_neighbors=5)</code></pre>
<p>Unsupervised Learning Estimators</p>
<p>Principal Component Analysis (PCA)</p>
<pre class="language-python"><code>from sklearn.decomposition import PCA
pca = PCA(n_components=0.95)</code></pre>
<p>K Means</p>
<pre class="language-python"><code>from sklearn.cluster import KMeans
k_means = KMeans(n_clusters=3, random_state=0)</code></pre>
<h3>Model Fitting</h3>
<h4>Supervised learning</h4>
<pre class="language-python"><code>lr.fit(X, y)
knn.fit(X_train, y_train)
svc.fit(X_train, y_train)</code></pre>
Unsupervised Learning
<pre class="language-python"><code>k_means.fit(X_train)
pca_model = pca.fit_transform(X_train)</code></pre>
<h3>Prediction</h3>
<p>Supervised Estimators</p>
<pre class="language-python"><code>y_pred = svc.predict(np.random.random((2,5)))
y_pred = lr.predict(X_test)
y_pred = knn.predict_proba(X_test))</code></pre>
<p>Unsupervised Estimators</p>
<pre class="language-python"><code>y_pred = k_means.predict(X_test)</code></pre>
<h3>Evaluate Your Model's Performance</h3>
<h4>Classification Metrics</h4>
<p>Accuracy Score</p>
<pre class="language-python"><code>knn.score(X_test, y_test)
from sklearn.metrics import accuracy_score
accuracy_score(y_test, y_pred)</code></pre>
<p>Classification Report</p>
<pre class="language-python"><code>from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred)))</code></pre>
<p>Confusion Matrix</p>
<pre class="language-python"><code>from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, y_pred)))</code></pre>
<h4>Regression Metrics</h4>
<p>Mean Absolute Error</p>
<pre class="language-python"><code>from sklearn.metrics import mean_absolute_error
y_true = [3, -0.5, 2])
mean_absolute_error(y_true, y_pred))</code></pre>
<p>Mean Squared Error</p>
<pre class="language-python"><code>from sklearn.metrics import mean_squared_error
mean_squared_error(y_test, y_pred))</code></pre>
<p>R2&nbsp;Score</p>
<pre class="language-python"><code>from sklearn.metrics import r2_score
r2_score(y_true, y_pred))</code></pre>
<h4>Clustering Metrics</h4>
<p>Adjusted Rand Index</p>
<pre class="language-python"><code>from sklearn.metrics import adjusted_rand_score
adjusted_rand_score(y_true, y_pred))</code></pre>
<p>Homogeneity</p>
<pre class="language-python"><code>from sklearn.metrics import homogeneity_score
homogeneity_score(y_true, y_pred))</code></pre>
<p>V-measure</p>
<pre class="language-python"><code>from sklearn.metrics import v_measure_score
metrics.v_measure_score(y_true, y_pred))</code></pre>
<h4>Cross-Validation</h4>
<pre class="language-python"><code>print(cross_val_score(knn, X_train, y_train, cv=4))
print(cross_val_score(lr, X, y, cv=2))</code></pre>
<h3>Tune Your Model</h3>
<h4>Grid Search</h4>
<pre class="language-python"><code>from sklearn.grid_search import GridSearchCV
params = {"n_neighbors": np.arange(1,3), "metric": ["euclidean", "cityblock"]}
grid = GridSearchCV(estimator=knn,param_grid=params)
grid.fit(X_train, y_train)
print(grid.best_score_)
print(grid.best_estimator_.n_neighbors)</code></pre>
<h4>Randomized Parameter Optimization</h4>
<pre class="language-python"><code>from sklearn.grid_search import RandomizedSearchCV
params = {"n_neighbors": range(1,5), "weights": ["uniform", "distance"]}
rsearch = RandomizedSearchCV(estimator=knn,
   param_distributions=params,
   cv=4,
   n_iter=8,
   random_state=5)
rsearch.fit(X_train, y_train)
print(rsearch.best_score_)</code></pre>
<h3>Going Further</h3>
<p>Begin with&nbsp;<a href="https://www.datacamp.com/community/tutorials/machine-learning-python/" data-ol-has-click-handler="">our scikit-learn tutorial for beginners</a>, in which you'll learn in an easy, step-by-step way&nbsp;how to explore handwritten digits data, how to create a model for it, how to fit your data to your model and how to predict target values. In addition, you'll make use of Python's data visualization library matplotlib to visualize your results.</p>
<p>&nbsp;</p>
<p>PS. Don't miss our&nbsp;<a href="https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python/" data-ol-has-click-handler="">Bokeh cheat sheet</a>, the&nbsp;<a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/" data-ol-has-click-handler="">Pandas cheat sheet</a>&nbsp;or the&nbsp;<a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/" data-ol-has-click-handler="">Python cheat sheet for data science</a>.&nbsp;</p>
</div>

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.

Machine Learning Cheat Sheets

Machine Learning Cheat Sheet

Keras Cheat Sheet: Neural Networks in Python

Scikit-Learn Cheat Sheet: Python Machine Learning