You will be equipped to implement AI, machine learning, and deep learning models for real-world applications, analyze data effectively, and develop solutions using frameworks like TensorFlow and Keras, preparing you for roles in AI and data science.

AI is the broader concept of machines simulating human intelligence, ML is a subset where systems learn from data, and Deep Learning is a specialized ML field using neural networks for complex tasks.

Supervised learning models are algorithms trained on labeled data to predict outcomes, such as regression models for continuous predictions or classification models like Decision Trees and SVMs.

Yes, deep learning can be applied in supervised learning using neural networks to handle tasks like image recognition, text classification, or speech recognition.

An example of ML is spam email detection using classification algorithms, while DL can be seen in facial recognition systems powered by convolutional neural networks (CNNs).

The three types are supervised models (trained on labeled data), unsupervised models (work with unlabeled data), and reinforcement models (learn through rewards and penalties).

AI ML models are algorithms that process data to perform tasks like classification, regression, clustering, and decision-making, enabling applications like predictive analytics and automation.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

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Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.