5. Discuss the tips and tricks, best practices and common pitfalls like a bias-variance tradeoff, data imbalance etc.
Chapter 4: Supervised Learning for Classification Problems-Advanced
Chapter Goal: cover advanced classification algorithms for supervised learning algorithms
No of pages:30-40
Sub - Topics:
1. Refresh classification problems for supervised learning
2. Examine gradient boosting and extreme gradient boosting, support vector machine and neural network
3. Compare the performance of all the algorithms
4. Discuss the best practices and common pitfalls, tips and tricks
Chapter 5: End-to-End Model Deployment
Chapter Goal: guide the reader on the end-to-end process of deploying a supervised learning model in production
No of pages:25-30
1. Meaning of model deployment
2. Various steps in the model deployment process
3. Preparations to be made like settings, environment etc.
4. Various use cases in the deployment
5. Practical tips in model deployment
Vaibhav Verdhan has 12+ years of experience in Data Science, Machine Learning and Artificial Intelligence. An MBA with engineering background, he is a hands-on technical expert with acumen to assimilate and analyse data. He has led multiple engagements in ML and AI across geographies and across retail, telecom, manufacturing, energy and utilities domains. Currently he resides in Ireland with his family and is working as a Principal Data Scientist.
Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets.
You’ll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you’ll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You’ll conclude with an end-to-end model development process including deployment and maintenance of the model.
After reading Supervised Learning with Python you’ll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner.
You will:
Review the fundamental building blocks and concepts of supervised learning using Python
Develop supervised learning solutions for structured data as well as text and images
Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit models
Understand the end-to-end model cycle from business problem definition to model deployment and model maintenance
Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using Python