About this Event
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Duration: 1 Full Day (9:00 AM – 5:00 PM)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, beverages, and light snacks included
Course Overview
Machine Learning & AI in Python equips you with the skills to understand, build, and evaluate predictive models using Python. This course covers essential machine learning concepts, including supervised and unsupervised learning, model evaluation, feature engineering, and an introduction to neural networks and deep learning. Through hands-on exercises and real datasets, you’ll move beyond theory and gain practical experience applying ML techniques to real-world problems.
Learning Objectives
By the end of this course, you will be able to:
- Understand key machine learning concepts and end-to-end workflows
- Build supervised and unsupervised models using scikit-learn
- Evaluate and compare models using appropriate performance metrics
- Apply feature engineering techniques to enhance model accuracy
- Gain foundational exposure to neural networks and deep learning concepts
- Use Python effectively for practical AI and machine learning tasks
Target Audience
Data scientists, machine learning engineers, software developers, and advanced Python users seeking to expand into AI and ML.
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Why Choose This Course?
This course is ideal if you want to extend your Python skills into machine learning and AI with confidence. The program emphasizes applied learning, allowing you to work with realistic datasets and industry-relevant use cases. Experienced instructors simplify complex topics—such as algorithms, model evaluation, and neural networks—through clear explanations and hands-on practice. By the end of the course, you’ll be equipped with practical tools, best practices, and the confidence to progress toward advanced AI and machine learning workflows.
📧 Contact us today to schedule a customized in-house, face-to-face session: [email protected]
Agenda
Module 1: Introduction to Machine Learning & AI
Info: • What is machine learning and AI?
• Role of Python in ML and AI
• Overview of ML workflow
• Activity
Module 2: Supervised Learning
Info: • 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
Info: • Clustering basics
• K-means and hierarchical clustering
• Use cases for dimensionality reduction (PCA)
• Case Study
Module 4: Model Training and Evaluation
Info: • Splitting datasets: train-test-validation
• Accuracy, precision, recall, F1-score, confusion matrix
• Cross-validation and tuning
• Activity
Module 5: Feature Engineering Essentials
Info: • Handling missing data and outliers
• Feature scaling and encoding
• Feature selection techniques
• Activity
Module 6: Introduction to Neural Networks
Info: • Understanding neurons and layers
• Basics of perceptrons and activation functions
• Overview of backpropagation
• Activity
Module 7: Deep Learning Concepts Overview
Info: • Understanding deep networks
• Brief intro to TensorFlow and Keras
• Practical examples in image and text processing
• Case Study
Module 8: Mini Project
Info: • Build a simple predictive model end-to-end
• Train, test, evaluate, and optimize
• Present insights and findings
• Activity
Event Venue & Nearby Stays
Regus - Boston - Federal Street, 101 Federal Street,#Suite 1900, Boston, United States
USD 519.25 to USD 622.95
