Machine Learning Fundamentals Journey
This path will introduce you to the core concepts and techniques of machine learning. You will learn about different types of ML, how to prepare data, build and evaluate models, and understand practical considerations for real-world applications.
Sessions
Data's Role: Prep & Features
Learn why data quality is crucial and how to clean, transform, and engineer features to prepare data for ML models.
10 min
Supervised Learning: Classification
Explore classification with Logistic Regression and Decision Trees, learning to predict categorical outcomes and evaluate their performance.
10 min
ML Unveiled: What & Why
Explore the definition of machine learning, its main categories, and real-world applications across various industries.
10 min
Supervised Learning: Regression
Dive into supervised learning with linear regression, understanding how models predict continuous numerical values and the underlying mechanics.
10 min
Unsupervised Learning: Clustering
Discover unsupervised learning by focusing on K-Means clustering and its applications in finding hidden patterns and grouping data points.
10 min
ML Model Evaluation
Understand how to evaluate model performance rigorously, identify the bias-variance tradeoff, and effectively tune hyperparameters.
10 min
Deep Learning: Neural Networks
Get an introduction to neural networks, the foundational building blocks of deep learning, and their basic architecture and function.
10 min
ML Ethics & Deployment
Learn about practical challenges in ML, including model interpretability, ethical considerations, and basic steps for deploying models.
10 min
What you'll achieve
Define machine learning and distinguish between its main types (supervised, unsupervised, reinforcement).
Explain the importance of data preprocessing and common techniques used.
Implement and interpret basic supervised learning models like linear and logistic regression.
Understand and apply fundamental unsupervised learning algorithms such as K-Means clustering.
Evaluate model performance using appropriate metrics and techniques like cross-validation.
Identify and address common challenges in ML, including overfitting and underfitting.
Describe the basics of neural networks and their role in deep learning.
Recognize ethical considerations and practical steps in deploying ML models.