Machine Learning Essentials Course Overview

The Machine Learning Essentials course is a comprehensive program designed for learners to gain a strong foundation in machine learning (ML). It covers the ML landscape, applications, and delves into different algorithms and models, including both supervised and unSupervised learning. With a practical approach, the course offers hands-on experience through labs in environments like Jupyter notebooks or R-Studio.

Participants will learn crucial concepts such as Statistics, Covariance, Correlation, and Error analysis, alongside combating Overfitting and Underfitting. The course emphasizes Feature engineering, Data preparation, and visualization to enhance model accuracy. It explores various predictive models, including Linear regression, Logistic regression, SVM, Decision trees, Random forests, and Naive Bayes. Clustering with K-Means, dimensionality reduction with PCA, and Recommendation systems using collaborative filtering are also key components. Through real-world use cases, learners will apply these concepts, culminating in a final workshop that solidifies their machine learning expertise.