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 Python Libraries:  Deep dive into essential Python libraries for Machine Learning such as Pandas for data manipulation, NumPy for numerical operations, Scikit-Learn for machine learning models, and Matplotlib for data visualization

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

 

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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Neural Networks:  Understand the basic concepts and architecture of neural networks, including perceptrons, activation functions, and feedforward networks. Hands-on experience in building a simple neural network from scratch using Python and basic libraries.

 

• 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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• Build ML Models: Advanced training on building robust Machine Learning models using modern frameworks like PyTorch and Keras. Learn to leverage transformers for cutting-edge applications

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

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GitHub Introduction: Learn the basics of version control and how to use GitHub for your projects, including repositories, branches, commits, and pull requests.​

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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.​

 

• 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.

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AWS Serverless Architectures: Gain insights into cloud architectures and serverless computing with AWS, including services like Lambda, S3, and SageMaker.

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​Cloud Deployment and Challenges: Understand the challenges of deploying models to the cloud, including scalability, latency, and cost management. Learn best practices for overcoming these challenges.

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8+ ML Projects: Complete three projects that can be used for college submissions. Example projects include house price prediction, spam email detection, handwritten digit recognition, customer sentiment analysis, movie recommendation system, or AI-based chatbots. Further, work on multiple projects to build a strong portfolio. Example projects include real-time fraud detection systems, personalized recommendation engines, or autonomous navigation systems.

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Resume Building, Interview Preparation, Mock Interviews: Guidance on building your resume, emphasizing your new skills and projects, and preparing for technical interviews. Comprehensive preparation for job interviews, including mock interviews, behavioural questions, and technical assessments.

 

Outcome:  By the end of this course, you will be proficient in building and deploying Machine Learning models, equipped with a professional resume, and ready for the industry with multiple projects to demonstrate your skills.