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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 three-tiered approach packed with hands-on examples and code.

Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered.

Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment.

Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem.

Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!

What You'll Learn

• 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.
• Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.
  

Who This Book Is For
IT professionals, analysts, developers, data scientists, engineers, graduate students

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

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: 50-60 Sub -Topics1. Brief on machine learning, definitions and concepts2. Industry standard for data mining processes - CRISP - DM and adoption in ML3. Brief on data processing, visualization, feature extractionengineering concepts4. 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. DL frameworks - theano, tensorflow, keras
PART II -  The Machine Learning PipelineChapter 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 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. Features - understanding yourv>2. Basic Feature engineering3. Extracting features from numeric, categorical variables4. Extracting features from datetimestamp variables5. 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 features10. Feature scaling and standardization11 Feature selection techniques12 Using forwardbackward selection techniques13 Using machine learning models like random forests14 Other methods
Chapter 5: Building, tuning and deploying modelsChapter 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 predictionsinsightsNo of pages : 50-60Sub - Topics:  1. 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 : 30-40Sub - 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 : 30-40Sub - 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 : 30-40Sub - 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 : 30-40Sub - 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 10: 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. Techniques - collaborative fv>iv>3. Analyzing tresights from music dataiv>4. Musicsong recommendations in action
Chapter 11: Forecasting stock and commodity pricesChapter 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 RNNsNo of pages : 40 - 50Sub - Topics:  1. Trend analysis2. Time series forecasting - ARIMAEWMA models3. Deep learning based forecasting - RNNLSTM models4. RegressionMC models if needed
Chapter 12: Image similarity, classification and generationChapter 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 modelsNo of pages : 50Sudiv>b - Topics:  ;iv>1. Image processing, similarity analysis2. Basic models - simple classification, dynamic time warping3. Image classification with deep learning models - CNNs, MLPs4. Image generation using generative adversial networks in deep learning (GANs) - if timescope permits