Machine Learning

ML is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make decisions based on data. Unlike traditional programming, where explicit instructions are provided, ML algorithms use statistical techniques to improve their performance as they are exposed to more data. Key types of ML include supervised learning (where the model is trained on labeled data), unsupervised learning (on unlabeled data), and reinforcement learning (where the model learns by interacting with its environment and receiving feedback).

• In-Depth Training: Dive into supervised and unsupervised learning, reinforcement learning, and other key ML methodologies.
• Tool Proficiency: Master popular ML tools and frameworks such as TensorFlow, Scikit-learn, and PyTorch.
• Real-World Applications: Work on projects that apply ML to practical scenarios, preparing you for industry challenges.