Supervised Learning with Python

Chapter 1: Introduction to Supervised LearningChapter Goal: Start the journey of the readers on supervised learning
No of pages: 30-40
Sub -Topics
1. Machine learning and how is it different from software engineering?

2. Discuss reasons for machine learning being popular
3. Compare between supervised, semi-supervised and unsupervised algorithms
4. Statistical methods to get significant variables
5. The use cases of machine learning and respective use cases for each of supervised, semi-supervised and unsupervised algorithms
Chapter 2: Supervised Learning for Regression AnalysisChapter Goal: Embrace the core concepts of supervised learning to predict continuous variables
No of pages: 40-50
Sub - Topics
1. Supervised learning algorithms for predicting continuous variables

2. Explain mathematics behind the algorithms
3. Develop Python solution using linear regression, decision tree, random forest, SVM and neural network
4. Measure the performance of the algorithms using r square, RMSE etc.
5. Compare and contrast the performance of all the algorithms
6. Discuss the best practices and the common issues faced like data cleaning, null values etc.
Chapter 3: Supervised Learning for Classification ProblemsChapter Goal: Discuss the concepts of supervised learning for solving classification problems
No of pages: 30-40
Sub - Topics:
1. Discuss classification problems for supervised learning

2. Examine logistic regression, decision tree, random forest, knn and naïve Bayes. Understand the statistics and mathematics behind each
3. Discuss ROC curve, akike value, confusion matrix, precision/recall etc
4. Compare the performance of all the algorithms
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 DeploymentChapter 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

About the Author: 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.