ProjectPro AI Newsletter: Jan 2024
Unlocking the potential of data science and big data requires more than just expertise — it requires a community committed to sharing knowledge, insights, and innovative solutions. So, we are introducing you to the ProjectPro AI Newsletter, where the frontier of data-driven projects meets ingenuity.
In this first edition, we delve into a treasure chest of new projects, insightful blogs, and engaging YouTube videos introduced in January by ProjectPro. From unraveling complex algorithms to decoding the latest industry trends, ProjectPro’s dedication to fostering learning and exploration in data science and big data shines through every piece of content they create. Join us as we navigate the vast landscape of innovation, empowerment, and knowledge dissemination in the next few sections
New Projects
Discover our latest hands-on projects in this section. Whether you’re diving into the buzzing topic in AI- Large Language Models or building practical applications in Python, these projects offer a chance to explore and understand the exciting world of data science and big data.
LLM Project to Build and Fine-Tune a Large Language Model
Large Language Models (LLMs) are revolutionizing natural language processing, empowering businesses across sectors. This project delves into LLM fundamentals, spotlighting prompt engineering and fine-tuning via techniques like LoRA. It demonstrates zero, one, and few-shot inferences, enhancing LLM functionality for targeted outputs. Leveraging OpenAI’s GPT-3.5 Turbo, it showcases Retrieval Augmented Generation (RAG) in a practical context — crafting an e-commerce chatbot with knowledge grounding, ensuring accuracy through real-time data verification. Aimed at providing comprehensive LLM insights and hands-on experience, this project covers dialogue summarization using knkarthick/dialogsum dataset and offers a step-by-step guide on constructing an RAG-based chatbot for online shopping.
Langchain Project for Customer Support App in Python
This project delves into the transformative power of Large Language Models (LLMs) in today’s data-driven era. Exploring the intricacies of LLMs, emphasizes optimizing their functionality through prompt engineering and fine-tuning techniques like LoRA. Through zero-shot, one-shot, and few-shot inferences, it enables LLMs to generate desired responses. This comprehensive guide navigates full fine-tuning and Parameter Efficient Fine Tuning (PEFT), enhancing LLM adaptation while conserving resources.
Utilizing OpenAI’s GPT-3.5 Turbo, the project employs Retrieval Augmented Generation (RAG) to craft a trustworthy e-commerce chatbot. Grounded in knowledge validation, RAG prevents misinformation by integrating real-world data sources for accurate responses, crucial for online shopping queries.
Note: Utilizing OpenAI’s API may require allocated free credits or additional purchases based on account status.
Learn to Build an End-to-End Machine Learning Pipeline — Part 1
This project addresses the pressing issue of truck shipment delays in the logistics industry. By accurately predicting delays, it aims to enhance operational efficiency, customer satisfaction, and cost-effectiveness. In its initial phase, the project utilizes PostgreSQL, MySQL on AWS RDS, and AWS Sagemaker to set up a robust pipeline for data retrieval, exploratory analysis, and feature engineering, laying the groundwork for subsequent predictive modeling.
With a focus on creating an end-to-end machine learning pipeline for truck delay classification, this project spans data fetching, feature store creation, preprocessing, and feature engineering. Broken into a series of videos, it covers fundamental concepts, database setup, exploratory analysis using SQL and Python, and the significance of feature stores like Hopsworks. The tech stack includes Python, SQL, NumPy, Pandas, AWS Sagemaker, and more, ensuring a comprehensive understanding of logistical predictive modeling.
Key components of this project revolve around database management, exploratory analysis, AWS SageMaker setup, feature store utilization, data retrieval, preprocessing, and storage. By amalgamating these stages, the project aims to equip individuals with the skills and insights necessary to construct effective predictive models for tackling truck delay challenges in logistics.
Learn to Build an End-to-End Machine Learning Pipeline — Part 2
This project targets the logistics industry’s challenge of delayed truck shipments, impacting operational costs and customer satisfaction. By accurately predicting delays, it aims to optimize resource allocation, enhance delivery schedules, and reduce costs incurred due to delays. Phase one focused on PostgreSQL, MySQL, AWS Sagemaker, and Hopsworks for data storage, retrieval, exploratory analysis, and initial feature group creation.
Part two delves deeper, concentrating on machine learning pipeline refinement. Emphasizing feature store data retrieval, model building with logistic regression, random forest, and XGBoost, it explores hyperparameter tuning, utilizing Weights and Biases for experimentation. Additionally, it covers model deployment via Streamlit on AWS, emphasizing cost-effective yet robust predictive modeling.
Utilizing Hopsworks’ feature store, Weights and Biases for experimentation, and AWS services like Sagemaker and EC2, this project aims to construct a comprehensive end-to-end machine learning pipeline. Data retrieval, model building, hyperparameter tuning, and Streamlit application deployment on AWS EC2 are core components, offering practical insights for tackling truck delay prediction in logistics.
New Blogs
Explore fresh insights in our new blogs on the latest trending topics in AI. Whether you’re just starting or looking for a bit more challenge, these blogs provide useful tips for your learning journey.
Practical Guide to Implementing Apache NiFi in Big Data Projects
Explore the world of Apache NiFi — unveil its purpose, role in Big Data projects, key features, core concepts, and architecture. Delve into the transformative capabilities of Apache NiFi and discover how it fuels Big Data initiatives. Ready yourself to apply this knowledge with hands-on projects tailored by ProjectPro for mastering Apache NiFi.
How to Learn Generative AI from Scratch in 2024?
This blog will let you dive into the significance of mastering Generative AI from the ground up and explore our roadmap for learning Generative AI in 2024. Uncover the top courses and certifications from industry giants like Google and Microsoft, guiding you through the realm of Generative AI. Elevate your skills with ProjectPro’s tailored approach to mastering Generative AI, empowering you to navigate this transformative field with confidence.
Top Careers in AI And Machine Learning For 2024
Explore the career landscape in AI through this blog that covers top career paths like Machine Learning Engineer, AI Product Manager, and Robotics Engineer. Discover insights on the highest-paying roles, companies hiring, and FAQs for those aspiring to excel in the AI industry. Get future-ready with ProjectPro’s guide to thriving careers in AI and Machine Learning.”
Top 10 PyCharm Project Ideas in Data Science
Whether you’re a novice or looking for intermediate-level challenges, we’ve handpicked projects to enhance your data science skills. From projects for beginners to those with source code, our blog offers a curated selection to support your learning journey. Explore the possibilities and boost your skills with practical, hands-on PyCharm projects.
New Videos on YouTube
This month, we’ve added a thought-provoking video- ‘An Approach to Real Life Data Science Interview Stage by Vivek Dani’ to the ProjectPro YouTube Channel. Join Vivek Dani, Research Software Engineer at Microsoft Research, as he shares insights on real-life Data Science interviews. Gain a deeper understanding of applied data scientist roles, interview preparation, and the distinctions between Microsoft Research and Microsoft IDC. Explore the nuances of machine learning roles, educational prerequisites, and essential skills for your ML journey. No frills, just valuable insights. Check out the video for a candid conversation on the realities of data science interviews.
If you want to stay updated with such relevant content in the domain of data science and big data, we highly recommend you follow ProjectPro’s LinkedIn page.
