Capitalizing Data Science Mathangi Sri Ramachandran
- Autor:
- Mathangi Sri Ramachandran
- Wydawnictwo:
- BPB Publications
- Ocena:
- Stron:
- 254
- Dostępne formaty:
-
ePubMobi
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Capitalizing Data Science
Unlock the Potential of Data Science and Machine Learning to Your Business and Organization
Key Features
Includes today's most popular applications powered by data science and machine learning technology.
A solid primer on the entire data science lifecycle, detailed with examples.
An integrated approach to demonstrating the use of Image Processing, Natural Language Processing, and Neural Networks in business. Description
Can you foresee how your company and its products will benefit from data science? How can the results of using AI and ML in business be tracked and questioned? Do questions like how do you build a data science team? keep popping into your head?
All these strategic concerns and challenges are addressed in this book.
Firstly, the book explores the evolution of decision-making based on empirical evidence. The book then helps compare the data-supported era with the current data-led era. It also discusses how to successfully run a data science project, the lifecycle of a data science project, and what it looks like. The book dives fairly in-depth into various today's data-led applications, highlights example datasets, discusses obstacles, and explains machine learning models and algorithms intuitively.
This book covers structural and organizational considerations for making a data science team. The book helps recommend the use of optimal data science organization structure based on the company's level of development. Finally, the book explains data science's effects on businesses by assisting technological leaders. What you will learn
Learn the entire data science lifecycle and become fluent in each phase.
Discover the world of supervised and unsupervised learning applications and structured and unstructured datasets.
Discuss NLP's function, its potential, and the application of well-known methods like BERT and GPT3.
Explain practical applications like automatic captioning, machine translation, and emotion recognition.
Provide a framework for evaluating your team's data science skills and resources. Who this book is for
Startups, investors, small businesses, product management teams, CxO and all developing businesses desiring to leverage a data science team to gain the most from this book. The book also discusses the potential of practical applications of machine learning and AI for the future of businesses in banking and e-commerce. Table of Contents
1. Data-Driven Decisions from Beginning to Now
2. Data Science Life Cycle Part 1
3. Data Science Life Cycle Part 2
4. Deep Dive into AI
5. Applying AI with Structured DataBanking
6. Applying AI with Structured Data
7. Applying AI with Structured DataOn-Demand Deliveries
8. AI in Natural Language Processing
9. Bringing It All Together
Includes today's most popular applications powered by data science and machine learning technology.
A solid primer on the entire data science lifecycle, detailed with examples.
An integrated approach to demonstrating the use of Image Processing, Natural Language Processing, and Neural Networks in business. Description
Can you foresee how your company and its products will benefit from data science? How can the results of using AI and ML in business be tracked and questioned? Do questions like how do you build a data science team? keep popping into your head?
All these strategic concerns and challenges are addressed in this book.
Firstly, the book explores the evolution of decision-making based on empirical evidence. The book then helps compare the data-supported era with the current data-led era. It also discusses how to successfully run a data science project, the lifecycle of a data science project, and what it looks like. The book dives fairly in-depth into various today's data-led applications, highlights example datasets, discusses obstacles, and explains machine learning models and algorithms intuitively.
This book covers structural and organizational considerations for making a data science team. The book helps recommend the use of optimal data science organization structure based on the company's level of development. Finally, the book explains data science's effects on businesses by assisting technological leaders. What you will learn
Learn the entire data science lifecycle and become fluent in each phase.
Discover the world of supervised and unsupervised learning applications and structured and unstructured datasets.
Discuss NLP's function, its potential, and the application of well-known methods like BERT and GPT3.
Explain practical applications like automatic captioning, machine translation, and emotion recognition.
Provide a framework for evaluating your team's data science skills and resources. Who this book is for
Startups, investors, small businesses, product management teams, CxO and all developing businesses desiring to leverage a data science team to gain the most from this book. The book also discusses the potential of practical applications of machine learning and AI for the future of businesses in banking and e-commerce. Table of Contents
1. Data-Driven Decisions from Beginning to Now
2. Data Science Life Cycle Part 1
3. Data Science Life Cycle Part 2
4. Deep Dive into AI
5. Applying AI with Structured DataBanking
6. Applying AI with Structured Data
7. Applying AI with Structured DataOn-Demand Deliveries
8. AI in Natural Language Processing
9. Bringing It All Together
Szczegóły książki
- ISBN Ebooka:
- 978-93-555-1158-4, 9789355511584
- Data wydania ebooka :
-
2024-12-18
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- Rozmiar pliku Mobi:
- 6.4MB
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