Machine Learning For Dummies. John Paul Mueller

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СКАЧАТЬ rel="nofollow" href="#ulink_d8e5291b-3a79-5090-bb44-79c6785ea664">Using Anaconda for Machine Learning Installing Anaconda on Linux Installing Anaconda on Mac OS X Installing Anaconda on Windows Downloading the Datasets and Example Code Chapter 5: Beyond Basic Coding in Python Defining the Basics You Should Know Storing Data Using Sets, Lists, and Tuples Defining Useful Iterators Indexing Data Using Dictionaries Chapter 6: Working with Google Colab Defining Google Colab Getting a Google Account Working with Notebooks Performing Common Tasks Using Hardware Acceleration Viewing Your Notebook Executing the Code Sharing Your Notebook Getting Help

      7  Part 3: Getting Started with the Math Basics Chapter 7: Demystifying the Math Behind Machine Learning Working with Data Exploring the World of Probabilities Describing the Use of Statistics Chapter 8: Descending the Gradient Acknowledging Different Kinds of Learning The learning process Optimizing with big data Chapter 9: Validating Machine Learning Considering the Use of Example Data Checking Out-of-Sample Errors Training, Validating, and Testing Optimizing by Cross-Validation Avoiding Sample Bias and Leakage Traps Chapter 10: Starting with Simple Learners Discovering the Incredible Perceptron Growing Greedy Classification Trees Taking a Probabilistic Turn

      8  Part 4: Learning from Smart and Big Data Chapter 11: Preprocessing Data Gathering and Cleaning Data Repairing Missing Data Transforming Distributions Creating Your Own Features Delimiting Anomalous Data Chapter 12: Leveraging Similarity Measuring Similarity between Vectors Using Distances to Locate Clusters Tuning the K-Means Algorithm Finding Similarity by K-Nearest Neighbors Chapter 13: Working with Linear Models the Easy Way Starting to Combine Features Mixing Features of Different Types Switching to Probabilities Guessing the Right Features Learning One Example at a Time Chapter 14: Hitting Complexity with Neural Networks Revising the Perceptron Representing the Way of Learning of a Network Introducing Deep Learning Chapter 15: Going a Step Beyond Using Support Vector Machines Revisiting the Separation Problem Explaining the Algorithm Classifying and Estimating with SVM Chapter 16: Resorting to Ensembles of Learners Leveraging Decision Trees Working with Almost Random Guesses Boosting Smart Predictors Averaging Different Predictors

      9  Part 5: Applying Learning to Real Problems Chapter 17: Classifying Images Working with a Set of Images Revising the State of the Art in Computer Vision Extracting СКАЧАТЬ