Python ML & AI Bootcamp: 1 Day Practical Workshop in New York, NY
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About the course:
Duration: 1 Full Day (9:00 AM β 5:00 PM)
Delivery Mode: Classroom / In-Person Workshop
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate Provided
Refreshments: Lunch, tea/coffee, and snacks included
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:
β’ Understand core machine learning concepts and workflows
β’ Build supervised and unsupervised models using scikit-learn
β’ Evaluate model performance using appropriate metrics
β’ Apply feature engineering techniques to improve predictions
β’ Gain basic knowledge of neural networks and deep learning
β’ 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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Agenda
Module 1: Introduction to Machine Learning & AI
β’ What is machine learning and AI?
β’ Role of Python in ML and AI
β’ Overview of ML workflow
β’ Activity
Module 2: Supervised Learning
β’ Regression vs classification
β’ Building basic linear and logistic models
β’ Using scikit-learn for model implementation
β’ Activity
Module 3: Unsupervised Learning
β’ Clustering basics
β’ K-means and hierarchical clustering
β’ Use cases for dimensionality reduction (PCA)
β’ Case Study
Module 4: Model Training and Evaluation
β’ Splitting datasets: train-test-validation
β’ Accuracy, precision, recall, F1-score, confusion matrix
β’ Cross-validation and tuning
β’ Activity
Module 5: Feature Engineering Essentials
β’ Handling missing data and outliers
β’ Feature scaling and encoding
β’ Feature selection techniques
β’ Activity
Module 6: Introduction to Neural Networks
β’ Understanding neurons and layers
β’ Basics of perceptrons and activation functions
β’ Overview of backpropagation
β’ Activity
Module 7: Deep Learning Concepts Overview
β’ Understanding deep networks
β’ Brief intro to TensorFlow and Keras
β’ Practical examples in image and text processing
β’ Case Study
Module 8: Mini Project
β’ Build a simple predictive model end-to-end
β’ Train, test, evaluate, and optimize
β’ Present insights and findings
β’ Activity
FAQs:
1. Do I need Python experience to attend?
Yes, a solid understanding of Python programming is required.
2. Will this course cover deep learning in detail?
No, only introductory concepts will be covered, but it builds a foundation for further study.
3. Do we get hands-on experience?
Yes, the course includes practical code exercises and real datasets.
4. Does the course include data preprocessing techniques?
Yes, essential feature engineering and data cleaning steps are included.
5. Can I use this knowledge for real projects?
Yes, you will learn practical workflows and tools to apply directly in projects.
6. Which machine learning tools will we use?
Primarily scikit-learn, along with introductions to TensorFlow/Keras.
7. Is this course beginner-friendly in ML?
Itβs ideal for those with Python knowledge but new to ML and AI workflows.
8. Is there a certification?
Yes, a Course Completion Certificate is provided.
9. Will we learn about model optimization?
Yes, tuning models and evaluating performance is part of the agenda.
10. Can this be customized for corporate teams?
Absolutely, we offer fully tailored content for team requirements.
