دانلود کتاب Python Machine Learning. A Crash Course for Beginners to Understand Machine learning, Artificial Intelligence, Neural Networks, and Deep Learning with Scikit-Learn, TensorFlow, and Keras.

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Introduction Chapter 1: The Basics of Machine Learning The Benefits of Machine Learning Supervised Machine Learning Unsupervised Machine Learning Reinforcement Machine Learning Chapter 2: Learning the Data sets of Python Structured Data Sets Unstructured Data Sets How to Manage the Missing Data Splitting Your Data Training and Testing Your Data Chapter 3: Supervised Learning with Regressions The Linear Regression The Cost Function Using Weight Training with Gradient Descent Polynomial Regression Chapter 4: Regularization Different Types of Fitting with Predicted Prices How to Detect Overfitting How Can I Fix Overfitting? Chapter 5: Supervised Learning with Classification Logistic Regression Multiclass Classification Chapter 6: Non-linear Classification Models K-Nearest Neighbor Decision Trees and Random Forests Working with Support Vector Machines The Neural Networks Chapter 7: Validation and Optimization Techniques Cross-Validation Techniques Hyperparameter Optimization Grid and Random Search Chapter 8: Unsupervised Machine Learning with Clustering K-Means Clustering Hierarchal Clustering DBSCAN Chapter 9: Reduction of Dimensionality The Principal Component Analysis Linear Discriminant Analysis Comparing PCA and LDA Conclusion