Foundations of Machine Learning

Fractal Analytics via Coursera

Go to Course: https://www.coursera.org/learn/foundations-of-machine-learning

Introduction

Construct Machine Learning models using the various steps of a typical Machine Learning Workflow

Apply appropriate metrics for various business problems to assess the performance of Machine Learning models

Develop regression and tree based Machine learning  Models to make predictions on relevant business problems

Analyze  business problems where unsupervised Machine Learning models  could be used to derive value from data

Syllabus

Introduction to Machine Learning

In this module, learners will unravel the magic of machine learning as they explore the significance of making predictions in various domains. They will gain a solid introduction to machine learning and its applications in different industries. The module will also cover essential concepts such as rule-based prediction and evaluation metrics, providing learners with a strong foundation for the rest of the course.

Building Your First Machine Learning (ML) Model for Synergix Solutions

This module focuses on guiding learners through the complete workflow of building their first machine learning model. Learners will dive into data preparation, exploratory data analysis (EDA), and feature engineering techniques. They will learn to build a K-Nearest Neighbors (KNN) model, understand model evaluation, and explore crucial considerations for deploying an ML model in real-world applications.

Evaluating Prediction Models

In this module, learners will delve into the intricacies of prediction models. They will explore evaluation metrics for both regression and classification models, gaining hands-on experience with practical implementations. The module will also cover data division techniques and benchmark performance, providing learners with a comprehensive understanding of how to effectively evaluate prediction models.

Linear and Logistic Regression

In this module, learners will embark on a comprehensive exploration of regression techniques. From understanding the principles of linear and logistic regression to their practical application, they will gain valuable insights into predictive modeling. With a focus on real-world scenarios, they will learn how to make predictions, interpret results, and optimize models.

Decision Trees for Synergix Solution

In this module, learners will navigate the intricate paths of decision trees. Decision trees offer a transparent yet powerful approach to classification and regression tasks. Learners will delve into the mechanisms of decision tree construction, learn to handle overfitting through pruning and regularization, and discover the art of fine-tuning decision trees for optimal results.

Introduction to Unsupervised Learning

In this module, learners will unlock the mysteries of unsupervised machine learning as they dive into clustering techniques. They will discover the power of KMeans and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) in grouping similar data points together. They will also explore how unsupervised learning revolutionizes data exploration, customer segmentation, and anomaly detection.