Python machine learning case studies : five case studies for the data scientist
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書誌事項
Python machine learning case studies : five case studies for the data scientist
（Books for professionals by professionals）
Apress, c2017
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注記
Includes index
内容説明・目次
内容説明
Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources.
Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You'll see machine learning techniques that you can use to support your products and services. Moreover you'll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs.
By taking a step-by-step approach to coding in Python you'll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems.
What You Will Learn
Gain insights into machine learning concepts
Work on real-world applications of machine learning
Learn concepts of model selection and optimization
Get a hands-on overview of Python from a machine learning point of view
Who This Book Is For
Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.
目次
- Chapter 1: Statistics and ProbabilityChapter Goal: Introduction and hands on approach to central limit theorem, distributions, confidence intervals, statistical tests, ROC curves, plots, probabilities, permutations and combinationsNo of pages: 70-80Sub -Topics1. Exploratory Data analysis2. Probability Distributions3. Concept of Permutations and Combinations4. Statistical tests5. Applications in the industry6. Case study Chapter 2: RegressionChapter Goal: Introduction and hands on approach to the concept of regression, linear regression models, non linear regression models.No of pages: 50-60Sub - Topics1. Concept of Regression2. Linear regression3. Polynomial order regression4. Statistical tests5. Applications in the industry6. Case study&
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- Chapter 3: Time series modelsChapter Goal: Introduction and hands on approach to concepts of trends, cycles, seasonal variations, anomaly detection, exponential smoothing, rolling moving averages, ARIMA, ARMA, over fitting.No of pages: 60-70Sub - Topics:1. Concept of trends, cycles, and seasonal variations2. Time series decomposition3. ARIMA, and ARMA models4. Concept of over fitting5. Statistical tests6. Applications in the industry7. Case study Chapter 4: Classification and ClusteringChapter Goal: Introduction and hands on approach to supervised, semi supervised and unsupervised models. Emphasis on Logistic regression, k-means, Support Vector Machines, Neural networksNo of pages: 80-90Sub - Topics:1. Concept of Classification and clustering2. Deep neur3. Support Vector Machines4. Concept of Gradient descent5. Statistical tests6. Applications in the industry7. Case study Chapter 5: Ensemble methodsChapter Goal: Introduction and hands on approach to Bagging, and Gradient BoostingNo of pages: 50-60Sub - Topics:1. Concept of ensemble methods2. Concept of Bagging 3. Concept of Gradient Boosting4. Statistical tests5. Applications in the industry6. Case study
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