Python and machine learning pdf
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- Introduction to Machine Learning with Python
- Practical Machine Learning with Python (eBook, PDF)
- Machine Learning with Python for Everyone
December 12th, To keep these chapters relevant and to improve the explanations based on reader feedback, we updated them to support the latest versions of NumPy, SciPy, and scikit-learn. One of the most exciting events in the deep learning world was the release of TensorFlow 2. Consequently, all the TensorFlow-related deep learning chapters have received a big overhaul.
Introduction to Machine Learning with Python
Jetzt bewerten Jetzt bewerten. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner.
The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.
Practical Machine Learning with Python follows a structured and comprehensive …mehr. DE Als Download kaufen. Jetzt verschenken. In den Warenkorb. Sie sind bereits eingeloggt.
Klicken Sie auf 2. Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering.
Dipanjan Sarkar is a Data Scientist at Intel, on a mission to make the world more connected and productive. He primarily works on data science, analytics, business intelligence, application development, and building large-scale intelligent systems.
Dipanjan has been an analytics practitioner for several years, specializing in statistical, predictive, and text analytics. Having a passion for data science and education, he is a Data Science Mentor at Springboard, helping people up-skill on areas like Data Science and Machine Learning.
Besides this, he occasionally reviews technical books and acts as a course beta tester for Coursera. Dipanjan's interests include learning about new technology, financial markets, disruptive start-ups, data science and more recently, artificial intelligence and deep learning.
PART I - Understanding Machine Learning Chapter 1: Machine Learning BasicsChapter Goal: This chapter familiarizes and acquaints readers with the basics of machine learning, industry standard workflows followed for machine learning processes and expands on the different types of machine learning and deep learning algorithmsNo of pages: Sub -Topics1.
Brief on machine learning, definitions and concepts2. Types of learning algorithms - supervised, unsupervised, reinforcement learning5. Advanced models - time series, deep learning6. Model building and validation concepts7. Applications of machine learningChapter 2: The Python Machine Learning EcosystemChapter Goal: This chapter introduces readers to the python language and the entire ecosystem built around machine learning with python tools, frameworks and libraries.
Overview and code samples are given for each tool to depict its usage and effectivenessNo of pages: 50 - 60Sub - Topics 1. Brief on Python 2. Why is Python effective for machine learning and data science3. Brief overview on the python ecosystem followed by data scientists includes anaconda distribution 4.
Reproducible research with ipython5. Data processing and computing with pandas, numpy, scipy6. Statistical learning with statsmodels7. ML frameworks - scikit-learn, pyml etc8. NLP frameworks - nltk, pattern, spacy9. Data Retrieval mechanisms crawling, databases, APIs etc 2.
Data attributes and features numeric, categorical etc 4. Data Wrangling cleaning, handling missing values, normalizing data 5. Data Summarization6. Data Visualization bar, histogram, boxplot, line, scatter etc Chapter 4: Feature Engineering and SelectionChapter Goal: This chapter focuses on the next stage in the ML pipeline, feature extraction, engineering and selection.
Readers will learn about both basic and advanced feature engineering methods for different data formats including numeric, text and images. We will also focus on methods for effective feature selectionNo of pages: 50 - 60Sub - Topics: 1. Basic Feature engineering3. Extracting features from numeric, categorical variables4.
Extracting Basic features from textual data bag of words 6. Advanced Feature engineering7. Extracting complex features from textual data word vectorization, tfidf, topic models 8. Extracting features from images pixels, edge detection, shapes 9.
Time series features Fitting and building models 2. Model evaluation techniques3. Model optimization methods like gradient descent4. Model tuning methodologies like cross validation, grid search5.
How to save and load models6. Deploying models in action PART III - Real-world case studies in applied machine learningChapter 6: Analyzing bike sharing trendsChapter Goal: This chapter will focus on a real-world case study of analyzing and predicting bike sharing trends with a focus on regression modelsNo of pages : Sub - Topics: 1.
Trend analysis2. Regression models3. Predictive analytics Chapter 7: Analyzing movie reviews sentimentChapter Goal: This chapter will focus on a real-world case study of analyzing sentiment for popular movie reviews using concepts and techniques from natural language processing, text analytics and classificationNo of pages : Sub - Topics: 1. Text Classification2. Natural language processing3. Sentiment analysis4. Comparing models and different features Chapter 8: Customer segmentation and effective cross sellingChapter Goal: This chapter will focus on a real-world case study of leveraging unsupervised learning and pattern recognition for solving problems in the retail industry like customer segmentation, cross selling and so onNo of pages : Sub - Topics: 1.
Clustering techniques2. Customer segmentation3. Pattern recognition and association rule mining4. Analyze potential product assoelling trendsChapter 9: Social network analysis - A Facebook case-studyChapter Goal: This chapter will focus on analyzing data from a popular social network - Facebook and acquaint readers to concepts from social network analysis and graph theoryNo of pages : Sub - Topics: 1.
Social network analysis2. Data retrieval and analysis from Facebook3. Concepts from graph theory applied in real-world data4. Useful visualizations from facebook data Chapter Analyzing music trends and recommentationsChapter Goal: This chapter will focus on a real-world case study of analyzing music trends and also providing music recommendations to users using concepts from recommender systems like collaborative filteringNo of pages : 40 - 50Sub - Topics: 1.
Recommender systems2. Image processing, similarity analysis2. Basic models - simple classification, dynamic time warping3. Retourenschein anfordern.
Practical Machine Learning with Python (eBook, PDF)
Machine learning is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this book, we want to show you how easy it can be to build machine learning solutions yourself, and how to best go about it. With the knowledge in this book, you can build your own system for finding out how people feel on Twitter, or making predictions about global warming. The applications of machine learning are endless and, with the amount of data available today, mostly limited by your imagination. This book is for current and aspiring machine learning practitioners looking to implement solutions to real-world machine learning problems. This is an introductory book requiring no previous knowledge of machine learning or artificial intelligence AI.
Note that while every book here is provided for free, consider purchasing the hard copy if you find any particularly helpful. In many cases you will find Amazon links to the printed version, but bear in mind that these are affiliate links, and purchasing through them will help support not only the authors of these books, but also LearnDataSci. Thank you for reading, and thank you in advance for helping support this website. Comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Learning and Intelligent Optimization LION is the combination of learning from data and optimization applied to solve complex and dynamic problems. Learn about increasing the automation level and connecting data directly to decisions and actions.
Machine Learning with Python for Everyone
Free download Read online. Description Table of Contents Details Hashtags Report an issue Book Description As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions - sometimes without final input from humans who may be impacted by these findings - it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all. This book will set you up with a Python programming environment if you don't have one already, then provide you with a conceptual understanding of machine learning in the chapter "An Introduction to Machine Learning. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari.
Sign in. Popular Python libraries are well integrated and provide the solution to handle unstructured data sources like Pdf and could be used to make it more sensible and useful. PDF is one of the most important and widely used digital media. PDFs contain useful information, links and buttons, form fields, audio, video, and business logic. As you know PDF processing comes under text analytics.