Python Machine Learning Case Studies (eBook)

Five Case Studies for the Data Scientist

Danish Haroon (Autor)

eBook Download: PDF

2017 | 1st ed.
XVII, 204 Seiten
Apress (Verlag)
978-1-4842-2823-4 (ISBN)

Infos

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.

Danish Haroon currently leads the Data Sciences team at Market IQ Inc, a patented predictive analytics platform focused on providing actionable, real-time intelligence, culled from sentiment inflection points. He received his MBA from Karachi School for Business and Leadership, having served corporate clients and their data analytics requirements. Most recently, he led the data commercialization team at PredictifyME, a startup focused on providing predictive analytics for demand planning and real estate markets in the US market. His current research focuses on the amalgam of data sciences for improved customer experiences (CX).

Inhalt

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 studyChapter 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<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 studyChapter 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 studyChapter 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

Produkt-Details

• Erscheint lt. Verlag: 27.10.2017
• Zusatzinfo: XVII, 204 p. 120 illus., 99 illus. in color.
• Verlagsort: Berkeley
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: Mathematik / Informatik ► Informatik ► Software Entwicklung
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Informatik ► Web / Internet
• Schlagworte: bagging • Clustering • Data Analysys • Machine Leraning • Python • Regression • Time series modelling
• ISBN-10: 1-4842-2823-5 / 1484228235
• ISBN-13: 978-1-4842-2823-4 / 9781484228234