Overview
Learn data science & machine learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!)
Syllabus
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- 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
Taught by
Maven Analytics, Chris Dutton and Joshua MacCarty
Tags
On-Demand
