AI & Machine Learning Using Python: 1 Day Session in Surrey

Group Discounts:

• Save 10% when registering 3 or more participants
• Save 15% when registering 10 or more participants

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

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.

Why is it right fit for you:

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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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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