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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

Design and Tailor this course

As per your team needs

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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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  • 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)
  • 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
  • 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
  • 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
  • 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
  • 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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