ML scikit learn and tensorflow study

scikit-learn wikipedia

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Use docker:

docker run -p 8888:8888 jupyter/scipy-notebook
check console for token 
browser http://192.168.99.100:8888/?token=<token_id>
open notebook 
from sklearn import datasets
digits = datasets.load_digits()
from sklearn import svm
clf = svm.SVC(gamma=0.001, C=100.)
clf.fit(digits.data[:-1], digits.target[:-1])
clf.predict(digits.data[-1:])

tensorflow wikipedia

TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.
Use docker:

docker run -p 8888:8888 jupyter/tensorflow-notebook
check console for token 
browser http://192.168.99.100:8888/?token=<token_id>
open notebook 
import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)

difference

TensorFlow is more low-level; basically, the Lego bricks that help you to implement machine learning algorithms whereas scikit-learn offers you off-the-shelf algorithms, e.g., algorithms for classification such as SVMs, Random Forests, Logistic Regression.

use keras with tensorflow

Keras is an open source neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit or Theano.[1] Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

Free and open-source software machine learning algorithms

CNTK Deeplearning4j ELKI H2O Mahout Mallet MLPACK MXNet OpenNN Orange scikit-learn Shogun Spark MLlib TensorFlow Torch / PyTorch Weka / MOA Yooreeka