Upon completion of this module, participants will possess the skills to efficiently collect, clean, and preprocess large datasets using popular tools such as Apache Spark and TensorFlow Data Validation. They will apply various data engineering techniques, including feature engineering and dimensionality reduction, to ensure data quality and optimize model performance. Additionally, students will work on real-life projects, addressing specific industry problems and utilizing machine learning and deep learning techniques to extract valuable insights from the data.

After completing this module, students will be capable of building and evaluating machine learning models using a variety of algorithms, such as decision trees, random forests, and gradient boosting. They will gain hands-on experience with libraries like scikit-learn and XGBoost to implement model training and evaluation procedures. Furthermore, learners will apply their knowledge to real-life projects, tackling industry-specific challenges and employing advanced machine learning techniques to derive meaningful predictions and recommendations.

At the end of this module, learners will have a comprehensive understanding of deep learning architectures and techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. They will gain practical experience with popular frameworks such as TensorFlow and PyTorch, implementing and fine-tuning deep neural networks for tasks like image classification, natural language processing, and sequence generation. Students will also engage in real-life projects, using deep learning techniques to address complex real-world problems and deliver innovative solutions.

Upon completion of this module, participants will be adept at using Amazon Web Services (AWS) for deploying and managing machine learning applications. They will gain hands-on experience with AWS services like Amazon SageMaker and Amazon Recognition, mastering the end-to-end process of building scalable machine learning pipelines on the cloud. Learners will work on real-life projects, leveraging AWS infrastructure to tackle industry-specific challenges and develop production-ready machine learning systems.

By the end of this module, students will master the development and deployment of generative AI models, including GANs, VAEs, and transformers, using popular frameworks like TensorFlow and PyTorch. They will gain hands-on experience with cutting-edge tools such as ChatGPT and Dell-E. Working on real-life projects, students will apply generative AI techniques to create realistic images, generate coherent text, and compose unique music pieces at the mid journey of their learning.

Project Topic: Indian Regional Language Translator

Business Domain: NLP & Translation

A regional language translator is a software application or service that allows users to translate text or speech from one regional language to another, typically between languages that are not commonly used outside of a specific region or country. Regional language translators are designed to address the language barriers that exist between different communities and languages within a region. Regional language translators use a variety of techniques to translate text, including statistical machine translation, rule-based translation, and neural machine translation. These techniques involve analyzing the structure and meaning of the source language and using that information to generate a translation in the target language.