Python ML & AI Essentials: 1 Day Virtual Session
Dive into ML and AI using Python—master supervised and unsupervised learning, model evaluation, and neural network basics in one day.
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About the course:
Duration: 1 Full Day (8 Hours)
Delivery Mode: Virtual Live
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
States Covered in CDT :
Start Time: 8:00 AM
USA – Alabama, Arkansas, Illinois, Iowa, Louisiana, Minnesota, Mississippi, Missouri, Oklahoma, Wisconsin, Kansas, Nebraska, North Dakota, South Dakota, Tennessee (part), Texas (part), Kentucky (part), Florida (part), Indiana (part), Michigan (part)
Canada – Manitoba, Saskatchewan (most areas), Ontario (part), Nunavut (part)
States Covered in EST :
Start Time: 9:00 AM
USA – Connecticut, Delaware, Florida (most areas), Georgia, Indiana (most areas), Kentucky (part), Maine, Maryland, Massachusetts, Michigan (part), New Hampshire, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee (part), Vermont, Virginia, West Virginia, Washington DC
Canada – Ontario (most areas), Quebec, New Brunswick, Nova Scotia, Prince Edward Island
Course Overview:
The Machine Learning & AI in Python course empowers you to understand, build, and evaluate predictive models using Python. You will learn the fundamentals of supervised and unsupervised learning, model evaluation metrics, feature engineering, and get a glimpse into neural networks and deep learning. With practical hands-on exercises, this course prepares you to transition from theory to real-world machine learning applications.
Learning Objectives:
By the end of this course, you will:
- Understand core machine learning concepts and workflows
- Build supervised and unsupervised models using scikit-learn
- Evaluate model performance using appropriate metrics
- Apply feature engineering techniques to improve predictions
- Gain basic knowledge of neural networks and deep learning
- Use Python for real-world AI and ML problem-solving
Target Audience:
Data scientists, ML engineers, developers, and advanced Python users.
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Good to know
Highlights
- 8 hours
- Online
Refund Policy
Location
Online event
Agenda
Module 1: Introduction to Machine Learning & AI
• What is machine learning and AI? • Role of Python in ML and AI • Overview of ML workflow • Activity
Module 2: Supervised Learning
• Regression vs classification • Building basic linear and logistic models • Using scikit-learn for model implementation • Activity
Module 3: Unsupervised Learning
• Clustering basics • K-means and hierarchical clustering • Use cases for dimensionality reduction (PCA) • Case Study