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Practical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent Systems - Softcover
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Inhaltsangabe
PART I - Understanding Machine Learning
Chapter 1: Machine Learning Basics
Chapter 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 algorithms
No of pages: 50-60
Sub -Topics
1. Brief on machine learning, definitions and concepts
al: 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 effectiveness2. Industry standard for data mining processes - CRISP - DM and adoption in ML
3. Brief on data processing, visualization, feature extraction\engineering concepts
4. Types of learning algorithms - supervised, unsupervised, reinforcement learning
5. Advanced models - time series, deep learning
6. Model building and validation concepts
7. Applications of machine learning
Chapter 2: The Python Machine Learning Ecosystem
Chapter Go
No of pages: 50 - 60
Sub - Topics
1. Brief on Python
2. Why is Python effective for machine learning and data science
3. Brief overview on the python ecosystem followed by data scientists (includes anaconda distribution)
4. Reproducible research with ipython
5. Data processing and computing with pandas, numpy, scipy
6. Statistical learning with statsmodels
7. ML frameworks - scikit-learn, pyml etc
8. NLP frameworks - nltk, pattern, spacy
9. DL frameworks - theano, tensorflow, keras
PART II - The Machine Learning Pipeline
Chapter 3: Processing, wrangling and visualizing data&
amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;Sub - Topics: 1. Data Retrieval mechanisms (crawling, databases, APIs etc)
2. Data processing (handling various forms of data - SQL, JSON, XML, Images)
3. Data attributes and features (numeric, categorical etc)
4. Data Wrangling (cleaning, handling missing values, normalizing data)
5. Data Summarization
6. Data Visualization (bar, histogram, boxplot, line, scatter etc)
Chapter 4: Feature Engineering and Selection
Chapter Goal: T
his 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 - 60
Sub - Topics:
1. Features - understanding your
v>2. Basic Feature engineering3. Extracting features from numeric, categorical variables
4. Extracting features from date\timestamp variables
5. Extracting Basic features from textual data (bag of words)
6. Advanced Feature engineering
7. Extracting complex features from textual data (word vectorization, tfidf, topic models)
8. Extracting features from images (pixels, edge detection, shapes)
9. Time series features
10. Feature scaling and standardization
11 Feature se
lection techniques12 Using forward\backward selection techniques
13 Using machine learning models like random forests
14 Other methods
Chapter 5: Building, tuning and deploying models
Chapter Goal: This chapter focuses on the final stage in the ML pipeline where readers will learn how to fit and build models on data features, how to optimize and tun
e model
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
- VerlagApress
- Erscheinungsdatum2018
- ISBN 10 1484232089
- ISBN 13 9781484232088
- EinbandPaperback
- SpracheEnglisch
- Kontakt zum HerstellerNicht verfügbar
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