by Henrik Brink, Joseph W. Richards, Mark Fetherolf | Released September 2016 | ISBN: None

"This is that crucial other book that many old hands wish they had back in the day." Beau Cronin, 21 Inc. Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. It will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems. Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand. Inside: Predicting future behavior Performance evaluation and optimization Analyzing sentiment and making recommendations No prior machine learning experience assumed. Learners should know Python. Henrik Brink, Joseph Richards, and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning. A comprehensive guide on how to prepare data for ML and how to choose the appropriate algorithms. Michael Lund, iCodeIT Very approachable. Great information on data preparation and feature engineering, which are typically ignored. Robert Diana, RSI Content Solutions NARRATED BY LISA FARINA Show and hide more

1. PART 1. THE MACHINE-LEARNING WORKFLOW
   • Chapter 1. What is machine learning? 00:09:36
   • Chapter 1. Using data to make decisions 00:07:55
   • Chapter 1. The machine-learning approach 00:10:04
   • Chapter 1. Five advantages to machine learning 00:04:28
   • Chapter 1. Following the ML workflow: from data to deployment 00:09:37
   • Chapter 1. Boosting model performance with advanced techniques 00:08:16
   • Chapter 2. Real-world data 00:07:17
   • Chapter 2. Which features should be included? 00:08:19
   • Chapter 2. How much training data is required? 00:06:43
   • Chapter 2. Preprocessing the data for modeling 00:09:59
   • Chapter 2. Simple feature engineering 00:04:38
   • Chapter 2. Using data visualization 00:08:09
   • Chapter 2. Density plots 00:04:57
   • Chapter 3. Modeling and prediction 00:09:14
   • Chapter 3. Finding the relationship between input and target 00:08:06
   • Chapter 3. Classification: predicting into buckets 00:08:00
   • Chapter 3. Classifying complex, nonlinear data 00:08:18
   • Chapter 3. Regression: predicting numerical values 00:08:19
   • Chapter 3. Summary 00:03:52
   • Chapter 4. Model evaluation and optimization 00:08:17
   • Chapter 4. The solution: cross-validation 00:05:55
   • Chapter 4. Evaluation of classification models 00:04:41
   • Chapter 4. Accuracy trade-offs and ROC curves 00:08:09
   • Chapter 4. Evaluation of regression models 00:06:24
   • Chapter 4. Model optimization through parameter tuning 00:08:54
   • Chapter 4. Summary 00:04:36
   • Chapter 5. Basic feature engineering 00:09:50
   • Chapter 5. Basic feature-engineering processes 00:08:59
   • Chapter 5. Feature selection 00:04:12
   • Chapter 5. Forward selection and backward elimination 00:07:04
   • Chapter 5. Summary 00:02:34
2. PART 2. PRACTICAL APPLICATION
   • Chapter 6. Example: NYC taxi data 00:05:48
   • Chapter 6. Defining the problem and preparing the data 00:05:14
   • Chapter 6. Modeling 00:08:56
   • Chapter 6. Summary 00:02:10
   • Chapter 7. Advanced feature engineering 00:07:29
   • Chapter 7. Topic modeling 00:08:19
   • Chapter 7. Content expansion 00:04:20
   • Chapter 7. Image features 00:04:50
   • Chapter 7. Extracting objects and shapes 00:07:06
   • Chapter 7. Time-series features 00:06:58
   • Chapter 7. Classical time-series features 00:08:46
   • Chapter 7. Summary 00:06:17
   • Chapter 8. Advanced NLP example: movie review sentiment 00:08:13
   • Chapter 8. So what’s the use case? 00:08:03
   • Chapter 8. Extracting basic NLP features and building the initial model 00:08:31
   • Chapter 8. Normalizing bag-of-words features with the tf-idf algorithm 00:08:34
   • Chapter 8. Advanced algorithms and model deployment considerations 00:10:38
   • Chapter 9. Scaling machine-learning workflows 00:07:36
   • Chapter 9. Subsampling training data in lieu of scaling? 00:09:23
   • Chapter 9. Scaling ML modeling pipelines 00:10:07
   • Chapter 9. Scaling predictions 00:06:02
   • Chapter 9. Summary 00:05:36
   • Chapter 10. Example: digital display advertising 00:06:04
   • Chapter 10. Feature engineering and modeling strategy 00:06:24
   • Chapter 10. Singular value decomposition 00:09:08
   • Chapter 10. Modeling 00:07:27
   • Chapter 10. Summary 00:08:44
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