EASY NATURAL LANGUAGE PROCESSING (NLP) IN PYTHON

Complete guide to practical NLP: spam detection, sentiment analysis, article spinners, and latent semantic analysis. Created by Lazy Programmer Inc. Last updated 5/2017 English What Will I Learn? Write your own spam detection code in Python Write your own sentiment analysis code in Python Perform latent semantic analysis or latent semantic indexing in Python Have an idea of how to write your own article spinner in Python Requirements Install Python, it’s free! You should be at least somewhat comfortable writing Python code Install numerical libraries for Python such as Numpy, Scipy, Scikit-learn, Matplotlib, and BeautifulSoup Some familiarity with PCA, Markov Models, …

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DATA SCIENCE: PRACTICAL DEEP LEARNING IN THEANO + TENSORFLOW

Take deep learning to the next level with SGD, Nesterov momentum, RMSprop, Theano, TensorFlow, and using the GPU on AWS. Bestselling Created by Lazy Programmer Inc. Last updated 6/2017 English What Will I Learn? Apply momentum to backpropagation to train neural networks Apply adaptive learning rate procedures like AdaGrad and RMSprop to backpropagation to train neural networks Understand the basic building blocks of Theano Build a neural network in Theano Understand the basic building blocks of TensorFlow Build a neural network in TensorFlow Build a neural network that performs well on the MNIST dataset Understand the difference between full gradient …

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CLUSTER ANALYSIS AND UNSUPERVISED MACHINE LEARNING IN PYTHON

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE. Bestselling Created by Lazy Programmer Inc. Last updated 6/2017 English What Will I Learn? Understand the regular K-Means algorithm Understand and enumerate the disadvantages of K-Means Clustering Understand the soft or fuzzy K-Means Clustering algorithm Implement Soft K-Means Clustering in Code Understand Hierarchical Clustering Explain algorithmically how Hierarchical Agglomerative Clustering works Apply Scipy’s Hierarchical Clustering library to data Understand how to read a dendrogram Understand the different distance metrics used in clustering Understand the difference between single linkage, complete linkage, Ward linkage, and UPGMA Understand …

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BAYESIAN MACHINE LEARNING IN PYTHON: A/B TESTING

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More Bestselling Created by Lazy Programmer Inc. Last updated 5/2017 English What Will I Learn? Use adaptive algorithms to improve A/B testing performance Understand the difference between Bayesian and frequentist statistics Apply Bayesian methods to A/B testing Requirements Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF) Python coding with the Numpy stack Description This course is all about A/B testing. A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more. A/B testing is all about comparing things. If you’re …

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ENSEMBLE MACHINE LEARNING IN PYTHON: RANDOM FOREST, ADABOOST

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python Created by Lazy Programmer Inc. Last updated 5/2017 English What Will I Learn? Understand and derive the bias-variance decomposition Understand the bootstrap method and its application to bagging Understand why bagging improves classification and regression performance Understand and implement Random Forest Understand and implement AdaBoost Requirements Differential calculus Numpy, Matplotlib, Sci-Kit Learn K-Nearest Neighbors, Decision Trees Probability and Statistics (undergraduate level) Linear Regression, Logistic Regresion Description In recent years, we’ve seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, …

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ADVANCED AI: DEEP REINFORCEMENT LEARNING IN PYTHON

The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks Created by Lazy Programmer Inc. Last updated 5/2017 English What Will I Learn? Build various deep learning agents Apply a variety of advanced reinforcement learning algorithms to any problem Q-Learning with Deep Neural Networks Policy Gradient Methods with Neural Networks Reinforcement Learning with RBF Networks Use Convolutional Neural Networks with Deep Q-Learning Requirements Know reinforcement learning basics, MDPs, Dynamic Programming, Monte Carlo, TD Learning Calculus and probability at the undergraduate level Experience building machine learning models in Python and Numpy Know how to build a feedforward, convolutional, …

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