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).
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.
|
Applies a case study-based approach to machine learning
Gives you insights into the core concepts of machine learning and optimization techniques
Uses Python as an aid to implement machine learning
Chapter 1: Statistics and Probability
Chapter Goal: Introduction and hands on approach to central limit theorem, distributions, confidence intervals, statistical tests, ROC curves, plots, probabilities, permutations and combinations
No of pages: 70-80
Sub –Topics
1. Exploratory Data analysis
2. Probability Distributions
3. Concept of Permutations and Combinations
4. Statistical tests
5. Applications in the industry
6. Case study
Chapter 2: Regression
Chapter Goal: Introduction and hands on approach to the concept of regression, linear regression models, non linear regression models.
No of pages: 50-60
Sub – Topics
1. Concept of Regression
2. Linear regression
3. Polynomial order regression
4. Statistical tests
5. Applications in the industry
6. Case study
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Chapter 3: Time series models
Chapter 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: 60-70
Sub - Topics:
1. Concept of trends, cycles, and seasonal variations
2. Time series decomposition
3. ARIMA, and ARMA models
4. Concept of over fitting
5. Statistical tests
6. Applications in the industry
7. Case study
Chapter 4: Classification and Clustering
Chapter Goal: Introduction and hands on approach to supervised, semi supervised and unsupervised models. Emphasis on Logistic regression, k-means, Support Vector Machines, Neural networks
No of pages: 80-90
Sub - Topics:
1. Concept of Classification and clustering
2. Deep
neur3. Support Vector Machines
4. Concept of Gradient descent
5. Statistical tests
6. Applications in the industry
7. Case study
Chapter 5: Ensemble methods
Chapter Goal: Introduction and hands on approach to Bagging, and Gradient Boosting
No of pages: 50-60
Sub - Topics:
1. Concept of ensemble methods
2. Concept of Bagging
3. Concept of Gradient Boosting
4. Statistical tests
5. Applications in the industry
6. Case study
Embrace machine learning approaches and Python to enable automatic rendering of rich insights. 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 Study 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 presented.
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 th
at you understand the data science approach to solving real-world problems.
You will:
- Gain insights into machine learning concepts
- Work on real-world applications of machine learning
- Get a hands-on overview to Python from a machine learning point of view
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 bookis 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 ForData scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.
Chapter 1: Statistics and Probability.- Chapter 2: Regression.- Chapter 3: Time series models.- Chapter 4: Classification and Clustering.- Chapter 5: Ensemble methods.
Über den Autor
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).
Inhaltsverzeichnis
Chapter 1: Statistics and Probability.- Chapter 2: Regression.- Chapter 3: Time series models.- Chapter 4: Classification and Clustering.- Chapter 5: Ensemble methods.
Klappentext
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. Yoüll see machine learning techniques that you can use to support your products and services. Moreover yoüll 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 yoüll be able to understand
the rationale behind model selection and decisions within the machine learning process. The bookis 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.
Applies a case study-based approach to machine learning
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Gives you insights into the core concepts of machine learning and optimization techniques
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Uses Python as an aid to implement machine learningrn
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