DYL-HP - AI-ML Advanced Python

You can access our application DYL-HP here.

AI-ML Advanced Python Course

Course Description

This course is aimed at equipping participants with advanced Python programming skills and a comprehensive understanding of data structures, algorithms, object-oriented principles, and concurrency. It also covers practical data analysis using Pandas, NumPy, and Matplotlib, and introduces foundational AI and machine learning concepts, including model development, optimization, and deployment. Participants will gain hands-on experience in building and presenting real-world machine learning applications

Learning Outcomes

By the end of this course, participants will be able to:

Demonstrate advanced proficiency in Python programming, including the use of list comprehensions, generators, lambda functions, recursion, and memoization to write efficient and optimized code.
Apply object-oriented programming (OOP) principles such as classes, inheritance, polymorphism, and encapsulation in the design and development of scalable and maintainable software systems.
Implement multithreading, multiprocessing, and asynchronous programming techniques to solve computationally intensive and concurrent problems effectively, with an understanding of Python’s Global Interpreter Lock (GIL).
Perform data manipulation, analysis, and visualization using libraries such as Pandas, NumPy, and Matplotlib, and conduct basic statistical evaluations to extract insights from complex datasets.
Understand the fundamentals of artificial intelligence (AI) and machine learning (ML), including the distinctions between supervised and unsupervised learning approaches.
Develop, train, and test machine learning models using Scikit-learn, applying suitable algorithms based on the nature of the data and problem.
Prepare and preprocess data for machine learning, including tasks such as data collection, cleaning, normalization, handling missing values, and feature engineering.
Evaluate the performance of machine learning models using appropriate metrics such as accuracy, precision, recall, F1-score, and confusion matrices to assess their effectiveness.
Optimize ML models through techniques such as feature selection, hyperparameter tuning, and the use of ensemble learning methods (e.g., bagging, boosting).
Deploy machine learning solutions in real-world scenarios by building and integrating APIs or web applications that deliver predictive capabilities in production environments.
Communicate technical results effectively by presenting machine learning workflows, model interpretations, and key insights in a manner accessible to both technical and non-technical audiences.
Break down and analyze complex, real-world problems, identifying appropriate tools, models, and methods for developing intelligent data-driven solutions.

To run the simulator, follow these steps:

Navigate to Online Python Compiler.
Copy and paste the code from the document into the editor.
Click the "Run" button below the code.
The output will be displayed in the console section.

You can modify the code to change the output as needed.

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🔹 Programming Language

Python: The most widely used language for AI/ML due to its simplicity and strong ecosystem of libraries.

🔹 Core Libraries

NumPy: For numerical operations and handling arrays/matrices.
Pandas: For data manipulation and preprocessing (cleaning, filtering, transforming data).
Matplotlib / Seaborn: For data visualization and exploring patterns.

🔹 Machine Learning Framework

Scikit-learn: A beginner-friendly and powerful ML library for supervised and unsupervised learning (e.g., regression, classification, clustering). Ideal for building classical ML models quickly.

🔹 Deep Learning Frameworks (for more advanced projects)

TensorFlow (with Keras API): A robust framework from Google for neural networks, ideal for deep learning tasks like image recognition and NLP.
PyTorch: Developed by Facebook, very popular in academia and research for dynamic computational graphs and deep learning projects.

🔹 Model Deployment Tools

Flask / FastAPI: Lightweight web frameworks to turn your ML model into an API for real-world use.
Streamlit / Gradio: Quick tools to create interactive web apps for demonstrating ML models without frontend development knowledge.

🔹 Jupyter Notebook