Day-16/90 | AI, DS and ML complete course for beginners in Tamil | Hire Ready | Python libraries(AI)
Day 16 of your Complete AI Course will introduce the most important Python libraries for AI, Machine Learning, and Data Science, explaining what each one does, when to use it, and how they connect together in real projects. This session helps beginners understand the Python AI ecosystem clearly so they know which tools to learn and why they matter for building end-to-end AI solutions. You will first explore NumPy, the foundational library for scientific computing in Python. NumPy gives powerful support for large, multi-dimensional arrays and matrices, which are essential for performing vectorized numerical operations and linear algebra, the backbone of almost all data science and machine learning workflows. Understanding NumPy arrays helps you work efficiently with data and is a key prerequisite for many other AI libraries. Next, you will learn about Pandas, the go-to tool for data cleaning, manipulation, and analysis. With its DataFrame structure, Pandas makes it easy to load datasets, handle missing values, filter rows, transform columns, and prepare data for modeling. This part of the session focuses on how NumPy and Pandas work together to form the core of any data science or AI pipeline, from raw data to feature-ready inputs. The lesson then introduces Scikit-learn, a simple and powerful library for traditional machine learning algorithms such as classification, regression, clustering, and dimensionality reduction. You’ll see how its consistent API makes it easy for beginners to train models, split data into train/test sets, evaluate performance, and run experiments quickly. You will understand where Scikit-learn fits in compared to deep learning frameworks, and why it is still a must-know library for many AI tasks. You will also be introduced to the major deep learning frameworks: TensorFlow: An end-to-end, open-source platform from Google for building, training, and deploying large-scale deep learning models and neural networks, suitable for production environments and scalable systems. PyTorch: A flexible deep learning framework from Meta AI, popular in research and academia for its dynamic computational graphs, intuitive Pythonic design, and strong GPU acceleration for fast experimentation. Keras: A high-level, user-friendly neural networks API that often runs on top of TensorFlow, allowing you to build and test deep learning models with minimal code, making it ideal for rapid prototyping and beginners stepping into deep learning. The session then covers visualization and model understanding tools: Matplotlib: The core plotting library in Python, used to create static, animated, and interactive visualizations like line charts, bar charts, histograms, and scatter plots. It provides very fine-grained control over plot appearance. Seaborn: Built on top of Matplotlib, offering a high-level interface for beautiful and informative statistical graphics. It simplifies the creation of complex plots such as heatmaps, pairplots, and distribution plots, helping you quickly explore relationships and patterns in your data. Finally, you will learn about XGBoost, a powerful library for gradient boosting on decision trees, widely used in structured/tabular data problems. XGBoost is known for its speed, accuracy, and strong performance in machine learning competitions, making it a favorite for Kaggle-style challenges and real-world classification and regression tasks involving tabular data. By the end of Day 16, you will know: What each major AI-related Python library does When to use NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, Matplotlib, Seaborn, and XGBoost How these tools combine to form a complete AI workflow from data loading and preprocessing to modeling, evaluation, visualization, and deployment
