Python ML & AI Bootcamp: 1 Day Practical Workshop in San Diego, CA

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Duration: 1 Full Day (8 Hours)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, Snacks and beverages will be provided during the session

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.

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

Data scientists, ML engineers, developers, and advanced Python users.

If you’re looking to take your Python programming skills into the realm of machine learning, this course is ideal. With a strong focus on applied learning and best practices, you’ll build models and analyze datasets that mirror real-world challenges. Our experienced instructors make complex concepts like algorithms and neural networks accessible through hands-on examples. This course helps you build confidence in working with machine learning tools and prepares you for advanced AI workflows.

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We offer tailored in-house training sessions designed to fit your organization’s specific learning needs. Want your developers to build predictive models or enrich your team’s data expertise? Let us customize a course that aligns with your business goals and projects.

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Info:
• What is machine learning and AI?
• Role of Python in ML and AI
• Overview of ML workflow
• Activity

Info:
• Regression vs classification
• Building basic linear and logistic models
• Using scikit-learn for model implementation
• Activity

Info:
• Clustering basics
• K-means and hierarchical clustering
• Use cases for dimensionality reduction (PCA)
• Case Study

Info:
• Splitting datasets: train-test-validation
• Accuracy, precision, recall, F1-score, confusion matrix
• Cross-validation and tuning
• Activity

Info:
• Handling missing data and outliers
• Feature scaling and encoding
• Feature selection techniques
• Activity

Info:
• Understanding neurons and layers
• Basics of perceptrons and activation functions
• Overview of backpropagation
• Activity

Info:
• Understanding deep networks
• Brief intro to TensorFlow and Keras
• Practical examples in image and text processing
• Case Study

Info:
• Build a simple predictive model end-to-end
• Train, test, evaluate, and optimize
• Present insights and findings
• Activity

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Host or Publisher Mangates