Fast-Track Python for AI & ML

Your Practical Guide to Core Concepts and Applications with Key Libraries and Techniques

Duration

1 Day (8 hours)

Level

Basic Level

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This 1-day, instructor-led course is tailored to equip participants with the necessary skills to effectively use Python in Machine Learning (ML) and Artificial Intelligence (AI) projects. It covers essential Python concepts, along with widely used libraries such as NumPy, Pandas, and Scikit-learn, focusing on their practical applications in real-world ML and AI tasks. By the end of the course, participants will have the skills to:

Set up and work within a Python environment optimized for ML and AI workflows
Manipulate and process data using Python libraries like NumPy and Pandas
Implement basic ML algorithms, covering both supervised and unsupervised learning methods
Gain an understanding of AI concepts, such as neural networks and deep learning, and build simple AI models using Python
Apply hands-on knowledge to solve real-world problems with ML and AI

The course features practical examples, coding exercises, and case studies, allowing participants to experience the practical side of Python in AI and ML projects.

Outcomes:

By the end of the course, participants will:

Gain a solid foundation in Python for ML and AI workflows
Be able to preprocess and manage datasets using essential Python libraries
Understand the foundational principles of machine learning, including the ability to implement key algorithms
Be introduced to deep learning concepts and neural networks
Walk away with practical skills that can be applied immediately to projects

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Professionals and students keen to venture into Machine Learning and Artificial Intelligence
Python developers aiming to integrate Python’s capabilities in AI and ML applications
Data scientists, analysts, or engineers who wish to upgrade their Python skills for handling ML and AI tasks more effectively

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Python Environment Setup & Overview for ML/AI (1 hour)

The growing role of Python in ML and AI
Setting up Python development environment (Anaconda, Jupyter Notebooks, IDEs)
Essential Python libraries (NumPy, Pandas, Scikit-learn, Keras/TensorFlow)
Built-in functions and modules (with focus on string manipulation, date, and math functions for data analytics)

Python Fundamentals Refresher (1 hour)

Basic Python syntax, data types, variables, operators, and control structures (loops, conditionals, error handling)
Functions and imports (including built-in and custom functions/modules)
Introduction to tuples, dictionaries, and sets for efficient data handling
Data filtering, sorting, dicing & slicing techniques in Python

Data Handling and Preprocessing in Python (2 hours)

In-depth with NumPy for numerical operations and Pandas for data manipulation
Data preprocessing techniques: Handling missing values, normalization, data encoding, and outlier detection
Exploratory Data Analysis (EDA) using Matplotlib and Seaborn
Data frames – merging, slicing, de-duplicating data
Feature engineering, data skewing, and dealing with class imbalance
Introduction to building data pipelines for end-to-end ML workflows

Machine Learning with Python (2 hours)

Understanding core machine learning concepts
Popular algorithms overview: Linear regression, decision trees, and k-means clustering
Training and evaluating models using Scikit-learn
Model selection techniques, cross-validation, and addressing column pruning
Hands-on exercise on real-word Dataset: Detecting and handling outliers and Implementing feature engineering

Introduction to AI & Deep Learning with Python (1 hour)

The AI landscape and real-world applications
Introduction to neural networks and deep learning concepts
Building simple AI models using Keras
Overview of reinforcement learning and its Python frameworks

Practical Hands-on Project & Case Study (1 hour)

End-to-end AI/ML project: Data preparation, model building, and evaluation
Step-by-step implementation of a ML model using Python
Real-world case study: Focus on feature engineering, data preprocessing, and model deployment

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A basic understanding of Python programming is expected
Familiarity with fundamental machine learning and AI concepts is helpful but not mandatory

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Fast-Track Python for AI & ML

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