Post Graduate Program in AI & Machine Learning

We dive into techniques for working with human language data, using tools like NLTK, spaCy, and even advanced models like Transformers. This helps us understand how machines process and analyze text.

We explore how computers recognize and understand images and videos. We cover image recognition, object detection, and use popular libraries like OpenCV and deep learning frameworks such as TensorFlow and PyTorch.

This is a really exciting area where we learn how agents can make decisions in an environment to maximize rewards. It’s a key part of how systems like self-driving cars learn over time.

We covered how to analyze and forecast data over time—perfect for stock market prediction or weather forecasting. We learn techniques like ARIMA, SARIMA, and even deep learning models like LSTM.

We can’t talk about AI and ML without handling massive datasets. We learn to use technologies like Hadoop and Spark to process and analyze big data efficiently.

Cloud platforms like AWS, Google Cloud, and Azure make scaling AI projects easier. We explore how to deploy machine learning models in the cloud and manage large-scale projects seamlessly.

In machine learning, optimizing your models is crucial. We cover methods like gradient descent, genetic algorithms, and simulated annealing to fine-tune model performance.

It’s important to know how to not just build models but also deploy them in real-world environments. We learn about using Docker, Kubernetes, and setting up automated CI/CD pipelines to make that process smooth.

This is increasingly critical. We learn about the ethical concerns in AI, like fairness, bias, and transparency. We explore the real-world impact of AI and why ethical considerations are essential.

One of the most important skills in ML is creating and selecting the right features for our models. We focus advanced techniques to improve our model’s accuracy and efficiency.

When our datasets get too large, reducing the number of features while retaining important information becomes vital. We learn methods like PCA and t-SNE to tackle this.

AI isn’t just theory; it’s used in healthcare, finance, retail, and autonomous systems. We look at how AI and ML are transforming industries, with practical projects to work with.

We learn learn about models like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), which are used to generate new data—think of creating art or even deepfakes.

Understanding why our model arrive at the decisions it does is important, especially for building trust in AI. We learn techniques like SHAP and LIME, which help interpret complex models.

Finally, we explore how AI is used in robotics for tasks like autonomous navigation and human-robot interaction, blending theory with cutting-edge applications.