Practical Machine Learning with Python

A complete guide of theoretical, technical, and hands-on implementations for practical applications of machine learning across diverse domains in the industry

Shows how data science and machine learning projects are executed in the real world

Provides readers with the essential skills to tackle their own real-world problems with machine learning

Autorentext

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 authoredseveral 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.

Inhalt
Chapter 1: Machine Learning Basics.- Chapter 2: The Python Machine Learning Ecosystem.- Chapter 3: Processing, Wrangling and Visualizing Data.-Chapter 4: Feature Engineering and Selection.- Chapter 5: Building, Tuning and Deploying Models.-Chapter 6: Analyzing Bike Sharing Trends.- Chapter 7: Analyzing Movie Reviews Sentiment.- Chapter 8: Customer Segmentation and Effective Cross Selling.- Chapter 9: Analyzing Wine Types and Quality.- Chapter 10: Analyzing Music Trends and Recommendations.- Chapter 11: Forecasting Stock and Commodity Prices.- Chapter 12: Deep Learning for Computer Vision.