Python Fundamentals:  Gain a solid understanding of Python, the most popular programming language in Machine Learning. Topics include data types, control structures, functions, and modules 

 

ML Algorithms with Examples:  Learn and implement basic Machine Learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, and K-Nearest Neighbors with practical examples. 

 

Transformers:  Learn about transformer models, their architecture, and applications in NLP tasks. Study how transformers have revolutionized the field with models like BERT, GPT, and T5. 

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NLP Techniques:  Understand Natural Language Processing techniques including text preprocessing, tokenization, stemming, lemmatization, and sentiment analysis. Explore tools like NLTK and SpaCy. 

 

Generative Models:  Study various generative models such as Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs), and their applications in image generation, data augmentation, and more. 

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Build a Chatbot:  Practical experience in building a chatbot using state-of-the-art techniques, integrating NLP and machine learning models to create an interactive conversational agent. 

 

Deploy Machine Learning Models:  Learn the end-to-end process of deploying ML models, including model serialization, API creation, and containerization with Docker. 

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Resume Building, Interviews Preparation, Mock Interviews: 

Get tips and guidance on how to build a strong resume, including how to highlight your technical skills and projects.  

Comprehensive preparation for job interviews, including mock interviews, behavioral questions, and technical assessments. 

 

3+ ML Projects:  Work on multiple projects to build a strong portfolio. Example projects include real-time fraud detection systems, customer sentiment analysis, AI-based chatbots etc.