Your journey into the world of Artificial Intelligence starts here. Master the fundamentals and build real-world projects from scratch.
Understand the core concepts of AI, Machine Learning, and Deep Learning. Explore real-world applications and the typical ML project lifecycle.
Grasp the beginner-level math and statistics that power ML, including basics of linear algebra, probability, and functions.
Set up your environment and learn essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization.
Learn to clean, handle missing values, scale features, and visualize datasets to prepare them for machine learning models.
Dive into regression and classification using fundamental algorithms like Linear Regression, Logistic Regression, and Decision Trees.
Explore clustering with K-Means to find hidden patterns in data, such as segmenting customers, and learn the basics of dimensionality reduction.
Measure your model's performance using metrics like Accuracy, Precision, and Recall. Understand overfitting vs. underfitting.
Get a gentle introduction to neural networks, understanding neurons, layers, and activation functions with TensorFlow & Keras.
Apply your knowledge by building practical projects like house price prediction, digit recognition, and spam detection.
Learn about ethical AI, popular frameworks, and potential career paths like Data Scientist, ML Engineer, and AI Researcher.