Sahil Panchal
Data Analyst | A Digital Detective
Machine Learning
Summary
Welcome to the Machine Learning section of my portfolio! Here, I’ll share an overview of my skills and experiences in the world of AI and machine learning. My passion for the field drives me to continually explore and implement new models and techniques that solve real-world problems. From internships where I developed predictive models to personal projects that pushed my abilities, machine learning has been at the heart of my professional journey.
I’m excited to showcase my expertise, and I hope my enthusiasm for this field inspires you as you explore my work!
Machine Learning Algorithms (ML) :
Machine learning has always fascinated me, and for over three years, I’ve been deeply engaged in learning and applying it to real-world problems. My journey began during my first internship as a Machine Learning Intern at Moba Mobile Automation, where I was thrown right into real projects. I worked with supervised learning algorithms and learned the nuances of improving model performance, turning theory into practical results.
Guided by my mentor, I strengthened my foundational knowledge through courses on platforms like Udemy, which solidified key concepts. Even now, I stay connected with my mentor, who encourages me to continually push my learning forward, tackle Kaggle challenges, and stay current in this fast-evolving field.
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Deep Learning (DL) :
Deep learning became a key area of focus for me when I realized its power in handling complex, visual data. I first encountered it during my studies in the Machine Learning Specialization by DeepLearning.AI, led by Andrew Ng. This course gave me a solid understanding of core machine learning concepts and introduced me to advanced areas like Deep Learning, Unsupervised Learning, and Reinforcement Learning. Eager to deepen my expertise, I enrolled in a follow-up Deep Learning specialization, which covered both foundational and cutting-edge topics, allowing me to experiment with TensorFlow, neural networks, and advanced model optimization techniques.
During my internship at Cinefly, I was tasked with leading a deep learning project that required extracting insights from video data, a new challenge for me. Leveraging my deep learning knowledge and GCP expertise, I worked with customer-generated marketing videos, applying techniques I had mastered in image data to a video-based environment. This experience pushed me beyond my comfort zone, expanding my skill set to cover video processing and real-world deep learning applications.
In addition to the formal courses, I honed my skills by following Daniel Bourke’s TensorFlow tutorials on YouTube, which helped me understand everything from basic TensorFlow operations to building and fine-tuning neural networks for real-world projects. This constant learning, combined with hands-on experience, has made me proficient in deep learning and excited about its potential.
Natural Language Processing (NLP) :
Natural Language Processing (NLP) became a significant focus during my university studies, particularly in a course on Big Data Analytics and Social Media analysis. I completed a project on sentiment analysis, utilizing Twitter, YouTube, and Spotify APIs to extract data for analysis. Using R programming, I processed the data and applied sentiment analysis techniques to gain insights into user opinions, tweets, ratings and feedbacks.
I later had the opportunity to work with NLP in my internship at Cinefly, where the data was more complex, involving both video and audio formats. I partitioned the data into visual and audio components and built a data pipeline to load the audio data into BigQuery. Using Python, I performed tasks such as sentiment analysis, text summarization, speech recognition, and text classification. This experience allowed me to refine my skills in NLP, expanding my knowledge from text-based analysis to multimodal data processing, further establishing my expertise in the field
Computer Vision (CV) :
Computer Vision is a critical branch of artificial intelligence that enables computers to interpret and understand visual data.
My experience in this area is both hands-on and technical. During my internship at Max Kelsen as a Machine Learning Intern, I worked on a computer vision pipeline for a medical software products, where I assisted in training, evaluating, and optimizing models to enhance the system’s performance. This real-world application deepened my expertise in image processing, and working closely with senior engineers sharpened my understanding of advanced computer vision techniques.
Other Skills :
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Programming Langauges : Python ( ) , R.
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Database : SQL ( )
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Data Visulization : Tableau ( ), Looker Studio
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Cloud Technology : GCP ( ), Azure (Familiar)
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Soft Skills ( )
Courses
I have actively enhanced my machine learning knowledge through various online resources, particularly YouTube. Notable channels like Krish Naik, Nicholas Renotte, and 3Blue1Brown have provided invaluable insights into key concepts and practical applications. While I don’t possess formal certificates from these resources, the hands-on experience and understanding I gained have significantly contributed to my skills in machine learning.
