Practical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent Systems

Practical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent Systems

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Product Details

ISBN-13: 9781484232064
Publisher: Apress
Publication date: 12/22/2017
Edition description: 1st ed.
Pages: 530
Product dimensions: 7.01(w) x 10.00(h) x (d)

About the Author

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. He holds a master of technology degree in Information Technology with specializations in Data Science and Software Engineering from the International Institute of Information Technology, Bangalore. He is also an avid supporter of self-learning, especially Massive Open Online Courses and also holds a Data Science Specialization from Johns Hopkins University on Coursera.

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. Dipanjan has also authored several books on R, Python, Machine Learning and Analytics, including Text Analytics with Python, Apress 2016. 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.

Raghav Bali has a master's degree (gold medalist) in Information

Technology from International Institute of Information Technology, Bangalore. He is a Data Scientist at Intel, where he works on analytics, business intelligence, and application development to develop scalable machine learning-based solutions. He has also worked as an analyst and developer in domains such as ERP, finance, and BI with some of the leading organizations in the world.

Raghav is a technology enthusiast who loves reading and playing around with new gadgets and technologies. He has also authored several books on R, Machine Learning and Analytics. He is a shutterbug, capturing moments when he isn't busy solving problems.

Tushar Sharma has a master’s degree from International Institute of Information Technology, Bangalore. He works as a Data Scientist with Intel. His work involves developing analytical solutions at scale using enormous volumes of infrastructure data. In his previous role, he has worked in the financial domain developing scalable machine learning solutions for major financial organizations. He is proficient in Python, R and Big Data frameworks like Spark and Hadoop.

Apart from work Tushar enjoys watching movies, playing badminton and is an avid reader. He has also authored a book on R and social media analytics.

Table of Contents

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
2. 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 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 effectiveness
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&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: 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 selection
No of pages: 50 - 60
Sub - Topics:
1. Features – understanding your2. Basic Feature engineering
3. 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 selection techniques
12 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 tune models and f learn ways of deploying models to use them in real-world scenarios for predictions\insights
No of pages : 50-60
Sub – Topics:
1. Fitting and building models
2. Model evaluation techniques
3. Model optimization methods like gradient descent
4. Model tuning methodologies like cross validation, grid search
5. How to save and load models
6. Deploying models in action

PART III – Real-world case studies in applied machine learning
Chapter 6: Analyzing bike sharing trends
Chapter Goal: This chapter will focus on a real-world case study of analyzing and predicting bike sharing trends with a focus on regression models
No of pages : 30-40
Sub – Topics:
1. Trend analysis
2. Regression models
3. 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 classification
No of pages : 30-40
Sub – Topics:
1. Text Classification
2. Natural language processing
3. Sentiment analysis
4. Comparing models and different features

Chapter 8: Customer segmentation and effective cross selling
Chapter 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 on
No of pages : 30-40
Sub – Topics:
1. Clustering techniques
2. Customer segmentation
3. Pattern recognition and association rule mining
4. Analyze potential product assoelling trends
Chapter 9: Social network analysis – A Facebook case-study
Chapter 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 theory
No of pages : 30-40
Sub – Topics:
1. Social network analysis
2. Data retrieval and analysis from Facebook
3. Concepts from graph theory applied in real-world data
4. Useful visualizations from facebook data
Chapter 10: Analyzing music trends and recommentations
Chapter 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 filtering
No of pages : 40 - 50
Sub – Topics:
1. Recommender systems
2. Techniques – collaborative fv>iv>3. Analyzing tresights from music dataiv>4. Music\song recommendations in action

Chapter 11: Forecasting stock and commodity prices
Chapter Goal: This chapter will focus on a real-world case study of trying to forecast stock and commodity price trends based on market data and using advanced models like time series models and deep learning models like RNNs
No of pages : 40 - 50
Sub – Topics:
1. Trend analysis
2. Time series forecasting – ARIMA\EWMA models
3. Deep learning based forecasting – RNN\LSTM models
4. Regression\MC models if needed

Chapter 12: Image similarity, classification and generation
Chapter Goal: This chapter will focus on trying to analyze a real-world image dataset and look at methods for image similarity, build image classifiers and generate images using innovative techniqueen advanced deep learning models
No of pages : 50
Sub – Topics: 1. Image processing, similarity analysis
2. Basic models – simple classification, dynamic time warping
3. Image classification with deep learning models – CNNs, MLPs
4. Image generation using generative adversial networks in deep learning (GANs) – if time\scope permits

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