Data Science & ML Essentials
Unlock the world of data science and machine learning. From exploring raw data to deploying intelligent models, this path covers the fundamental concepts and practical techniques you need to understand how AI works.
Sessions
EDA Basics
After this session, you'll be able to explain how to start investigating a new dataset using exploratory data analysis techniques.
5 min
Predicting Categories & Numbers
You'll understand the difference between predicting categories versus numbers, and how models learn from data.
5 min
ML Model Evaluation
You'll learn how to tell if a machine learning model is actually good at its job, and how to spot when it's just 'cheating'.
5 min
ML Deployment & Monitoring
You'll understand how machine learning models move from being experiments to being used in real-world applications, and what happens once they're 'live'.
5 min
Deep Learning Unveiled
You'll get a grasp of what 'deep learning' really means and how neural networks power some of today's most advanced AI.
5 min
Feature Engineering
You'll learn how to transform raw, messy data into clean, powerful 'features' that machine learning models can actually use.
5 min
What you'll achieve
Explain the importance of Exploratory Data Analysis (EDA) and apply common techniques like visualization and statistical summaries.
Describe how feature engineering transforms raw data into meaningful inputs for machine learning models.
Differentiate between supervised and unsupervised learning and identify appropriate model types for various problems.
Evaluate machine learning models using key metrics and understand the concepts of overfitting and underfitting.
Grasp the fundamental concepts of deep learning, including neural networks and their architecture.
Understand the basic patterns for deploying and monitoring machine learning models in real-world applications.