Machine Learning & Data Science: The Complete Visual Guide

Overview

This course is for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science.

Syllabus

• Introduction to Machine Learning and Data Science
Overview of Machine Learning
Key Concepts in Data Science
Applications and Impact on Everyday Life
• Understanding Data
Types of Data: Structured vs. Unstructured
Data Collection Methods
Data Cleaning and Preprocessing Basics
• Data Visualization
Importance of Data Visualization
Common Tools and Techniques
Creating Basic Visualizations
• Introduction to Machine Learning Algorithms
Supervised vs. Unsupervised Learning
Common Algorithms: Linear Regression, Decision Trees, k-Means Clustering
Introduction to Model Evaluation
• Building a Simple Machine Learning Model
Choosing the Right Tools (Python, R Basics)
Step-by-Step Guide to Building a Model
Evaluating Model Performance
• Introduction to Neural Networks and Deep Learning
Basics of Neural Networks
Overview of Deep Learning and Its Applications
• Ethical Considerations in Machine Learning
Bias in Data and Models
Privacy Concerns and Data Security
Social Implications of AI
• Machine Learning Tools and Libraries
Introduction to Popular Libraries (Scikit-Learn, TensorFlow)
Setting Up a Basic Environment
• Hands-On Projects
Simple Classification Project
Small Scale Predictive Analysis
Data Storytelling and Report Creation
• Course Summary and Next Steps
Recap of Key Concepts
Resources for Further Learning
Career Pathways in Machine Learning and Data Science

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Taught by

Maven Analytics, Chris Dutton and Joshua MacCarty

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Subjects

Data Science