Foundations of Machine Learning (Coursera)

By the end of this course, you will:

1. Grasp the fundamental principles of machine learning and its real-world applications.

2. Construct and evaluate machine learning models, transforming raw data into actionable insights.

3. Navigate through diverse datasets, extracting meaningful patterns that drive decision-making.

4. Apply machine learning strategies to varied scenarios, expanding your problem-solving toolkit.

This course equips you with the foundation to thrive as a machine learning enthusiast, data-driven professional, or someone ready to explore the dynamic possibilities of machine learning.

This course is part of the Fractal Data Science Professional Certificate.

What you'll learn

- 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.