Day-16/90 | AI, DS and ML complete course for beginners(English) | Hire Ready | Python libraries(AI)

Day 16 – Top Python Libraries for AI, Machine Learning and Data Science | AI Course in English Day 16 of your Complete Artificial Intelligence (AI) Course in English explores the most important Python libraries for AI, Machine Learning, and Data Science. This session gives you a clear roadmap of which tools to focus on, what each library is used for, and how they all connect to build real-world AI projects from data to deployment. If you are serious about becoming an AI engineer, data scientist, or ML developer using Python, these libraries are your core toolkit. You’ll start with NumPy, the fundamental package for scientific computing in Python. NumPy provides high-performance support for large, multi-dimensional arrays and matrices, along with a rich collection of mathematical functions. You’ll understand why almost every AI and data science library you use—like Pandas, Scikit-learn, TensorFlow, and PyTorch—depends on NumPy under the hood. Mastering NumPy’s array operations and vectorization is the first step toward writing efficient numerical code in Python. Next, you’ll learn about Pandas, the go-to library for data cleaning, manipulation, and analysis. Using its powerful DataFrame structure, you can load datasets, handle missing values, filter and sort data, create new columns, and prepare clean features for machine learning models. You’ll see how Pandas sits at the center of the data pipeline, bridging raw data and model-ready inputs in any AI or ML project. The session then introduces Scikit-learn, one of the most popular libraries for traditional machine learning. You’ll discover how Scikit-learn provides a simple and consistent API for tasks such as classification, regression, clustering, and dimensionality reduction. You’ll see how easy it is to split data into train/test sets, fit models, make predictions, and evaluate accuracy with just a few lines of code. Scikit-learn is ideal for beginners and professionals working on tabular data, baseline models, and rapid experimentation. You’ll then move into the world of deep learning with three key frameworks: TensorFlow – An end-to-end, open-source platform from Google for building, training, and deploying large-scale deep learning models and neural networks. It is widely used in production systems for its scalability and deployment tools. PyTorch – A flexible deep learning framework from Meta AI, favored in research and academia for its dynamic computation graphs, intuitive Pythonic design, and strong GPU acceleration. It lets you experiment and debug models more easily. Keras – A high-level neural network API that simplifies deep learning model creation. Often running on top of TensorFlow, Keras allows you to build and train complex models with minimal code, making it perfect for beginners and rapid prototyping. Visualization is a crucial part of AI and data science, so you will also explore: Matplotlib – The foundational plotting library in Python, used to create static, animated, and interactive visualizations including line plots, bar charts, histograms, scatter plots, and more. It gives you full control over every visual element. Seaborn – A higher-level library built on top of Matplotlib that makes it easy to create beautiful and informative statistical graphics. With Seaborn, you can quickly build heatmaps, pairplots, distribution plots, and relationship plots to explore patterns in your data. Finally, you’ll learn about XGBoost, a powerful and highly optimized implementation of gradient boosting on decision trees. XGBoost is famous for its speed and performance on structured/tabular data, and is widely used in Kaggle competitions and real-world ML solutions for classification and regression tasks. It often outperforms many other models when working with well-structured features. By the end of Day 16, you will: Know the key Python libraries for AI and ML and what each one is best at Understand how to use NumPy and Pandas for data handling and numerical computation Recognize when to choose Scikit-learn, XGBoost, or deep learning frameworks like TensorFlow/PyTorch/Keras See how Matplotlib and Seaborn help you visualize and understand your data and model results Have a clear learning path to become productive with the Python AI ecosystem