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Gopal Chitalia
Kaggle tutor
US$20.00 /15 minI am [Gopal Chitalia](https://scholar.google.com/citations?user=QncMQEIAAAAJ&hl=en), a graduate student at Purdue University and working with Prof. [Jan Anders Mansson](https://www.purdue.edu/engineering/mdlab/mansson/) on fault detection in motors using deep learning in a joint project with Wistron. Additionally, I'm working with Prof. [Junjie Qin](https://engineering.purdue.edu/people/junjie.qin.1) on transfer learning application using LLMs for optimizing power networks. I have had the privilege of working at [Center for Building Science Lab](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcbs.iiit.ac.in%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=UCjKsb98EbNq8jGvbf%2F5TEETyHMI1gnMx20lLBeQmog%3D&reserved=0) under the supervision of Prof. [Vishal Garg](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.co.in%2Fcitations%3Fuser%3DvyH26MIAAAAJ%26hl%3Den&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=pDBa4RZtT0eg4rN%2FdvVtv3q%2F6goK1EEVT6%2F6UeGPcw4%3D&reserved=0). I have previously been a Research Assistant/Visiting ML Scholar at the [Smart Grid Research Unit](https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.sgru.eng.chula.ac.th%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=UAp49aqIF2oxM3WIO5IS68gTyMLeYsKR5ENaosJMhvk%3D&reserved=0) (SGRU) under [Manisa Pipattanasomporn](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.com.sg%2Fcitations%3Fuser%3D4W2KIQkAAAAJ%26hl%3Den&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Pey86yYp3KpRPj9d9a97nzxLxQGMkQrN%2BFASqUqyheQ%3D&reserved=0) (Adjunct Faculty, Virginia Tech). In addition to my academic pursuits, I have also contributed to the industry in the domains of Energy Efficiency, IoT, and Machine Learning. I have worked at [ClevAir](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclevair.io%2Fen%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=nonCkudNp3cFUL2CNs6rMHm0HyQXSnh9lhoJ76s5xus%3D&reserved=0) (Norway) as a **Data Scientist** and Growthworks.ai (CA, USA) as an **ML Scientist/Energy Demand Expert**. My research work on predicting short/long-term energy prediction in office/residential buildings using machine learning is published in [Applied Energy](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fabs%2Fpii%2FS0306261920309223%3Fvia%253Dihub&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=V%2Bq5MVSeJ7deNpE%2F%2FEP1PYrsrEVWEH%2FwNK1yAG4CPlM%3D&reserved=0). The results improve the state-of-the-art results by 20-40%. I am also an active reviewer at Applied Energy. Furthermore, I have collaborated on a building-level dataset paper which has been published in [Nature Scientific Data](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nature.com%2Fsdata%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=rtv6fVxtvuYsk5NAwwR8ujauC1AfX9cs32w4evIj%2BZU%3D&reserved=0). Previously, I have also worked with Prof. [Jyotirmay Mathur](https://scholar.google.co.in/citations?user=AmGPPL4AAAAJ&hl=en) at the [Centre for Energy & Environment](http://www.mnit.ac.in/dept_cree/index.php), MNIT Jaipur on Predicting time ahead heating/cooling energy demand HVAC systems. Moreover, as an independent research student, I also collaborated with Prof. [Praveen Paruchuri](https://scholar.google.com/citations?user=ILUqgKEAAAAJ&hl=en) of [Machine Learning Lab](https://mll.iiit.ac.in/), IIIT-H for Reinforcement Learning (RL) applications for controlling HVAC systems.
- Kaggle
- HTML/CSS
- Python
- C++
- SQL
- Django
- Database
- Bash
- Web Scraping
- TypeScript
- Docker
- JavaScript
- Research
- Postman
- Dash
- Airflow
- Mobile App Development
- Performance Optimization
- PyTorch
- Fastapi
- AWS
- Large Language Models
- Git
- Selenium
- NumPy
- Version control
- GitHub
- Pandas
- Machine learning
- Data Science
- Data Visualization
- Reinforcement Learning
- Deep Learning
- Data analytics
- TensorFlow
- Keras
Sébastien Arnaud
Kaggle tutor
US$50.00 /15 minSébastien Arnaud is a French Software Architect, Engineer, and Inventor. With a passion for Data Science, in particular Machine Learning, General Game playing, Natural Language Processing and Text Analytics, Sébastien is an entrepreneur, who loves turning ideas into products, data into insights.
Simmi Mourya
Kaggle tutor
US$20.00 /15 minI I have six years of Python experience and three years of research experience in solving complex Deep Learning problems. Ground Rules: 1. Payments will only be accepted via Codementor platform. 2. I will refund the amount if you are not satisfied. 3. max( myrate, yourrequestRate ) will be applicable.
- Kaggle
- HTML/CSS
- Angular
- Design
- Data Mining
- Single-Page Applications
- Research
- Mentoring
- Caffe
- Keras
- High-Performance Computing
- Python
- NumPy
- Matplotlib
- Pandas
- Machine learning
- Scipy
- Computer Vision
- Data Science
- Data Visualization
- Neural Networks
- Cython
- Scientific Computing
- Deep Learning
- Artificial Neural Networks
- Shrine
- Data modeling
- PyTorch
- fastai
- AI
Tushar Gupta
Kaggle tutor
US$15.00 /15 min
I have good knowledge of predictive analysis, machine learning methodologies, modeling and cluster analysis of large datasets. I am proficient in utilizing SQL in RDBMS concepts. Experience in project documentation (functional and technical), developers and assisting team with project reports and status reports. Highly motivated learner with great team working skills Strong interpersonal, leadership and customer service skills. I have published paper at BMVC 2017: https://arxiv.org/abs/1710.05158 Deep Learning Blogger: https://medium.com/@tushar20 Technical Skills: Tools: Lucene, Latex, Eclipse Languages: Java, C, C++, R, Python Machine Learning Libraries: TensorFlow, Scikit-Learn, Theano, Keras Relational Database Management Systems: MySQL Web framework: HTML, CSS, JavaScript
Puneet Jain
Kaggle tutor
US$20.00 /15 minLove to create products which enable smarter/automated decision-making using data at scale. I like going into new areas and tackling problems that can be solved by applying data science.
Rajesh Shreedhar Bhat
Kaggle tutor
US$30.00 /15 minHello, I work as a Data Scientist at Walmart Labs, Bengaluru, India, and have 4 years of experience now. At Walmart, I am focused on building reusable machine/deep learning solutions that can be used across various business domains. I have research publications in the field of NLP and Vision, which are published at top tier conferences such as CoNLL, ASONAM, etc.. and have filed 6 US patents in Retail space leveraging AI & ML. Also, in my free time I participate in Kaggle competitions and now I am a Kaggle Competitions Expert(World Rank 966/122431) with 3 Silver and 2 Bronze medals. I have been speaker in highly recognized conferences/meetups such as Spark AI Summit, Data Hack Summit, Kaggle days meet up – Senior Track, etc .. Apart from this, I am a mentor for Udacity Deep learning & Data Scientist Nanodegree programs for the past 3 years and have conducted ML & DL workshops in GE Healthcare, IIIT Kancheepuram and many other places. For more details: https://rajesh-bhat.github.io
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“I appreciated that Pazia helped me break down complex interview problems. She’s great at explaining difficult concepts clearly and logically, and never rushes through anything. In addition to technical prep. She also helped me structure and prepare for behavioral interview questions. Her thoughtful guidance made me feel more confident and well-prepared overall.“
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Pazia Bermudez-Silverman
Kaggle tutor
“My session with Dejan went great. Dejan is very patient and helpful. He really understands the underlying concepts of DSA and prepared me very well for my interview. He not only teaches the concepts for interviews but also how to communicate effectively in interviews.“
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Dejan B.
Kaggle tutor
“Studying with Precious is always interesting. He is truly a tutor who imparts the skills you need when working in a company. Your level doesn’t matter, because that is a path of growth; what really counts is the mindset and being familiar with real situations that you may encounter during programming.“
none / Jun 2025
Nwoko Onyekachi Precious
Kaggle tutor
“I highly recommend Pazia as a tutor. She consistently looked for the most effective ways to help me grow, both technically and in my interview preparation. It was clear that she genuinely cared about helping me succeed and always tailored her approach to what would benefit me the most. Her dedication and insight made a big impact on my learning and confidence.“
Stella Chang / Jun 2025
Pazia Bermudez-Silverman
Kaggle tutor
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Frequently asked questions
How to learn Kaggle?
Learning Kaggle effectively takes a structured approach, whether you're starting as a beginner or aiming to improve your existing skills. Here are key steps to guide you through the learning process:
- Understand the basics: Start with the fundamentals of Kaggle. You can find free courses and tutorials online that cater specifically to beginners. These resources make it easy for you to grasp the core concepts and basic syntax of Kaggle, laying a solid foundation for further growth.
- Practice regularly: Hands-on practice is crucial. Work on small projects or coding exercises that challenge you to apply what you've learned. This practical experience strengthens your knowledge and builds your coding skills.
- Seek expert guidance: Connect with experienced Kaggle tutors on Codementor for one-on-one mentorship. Our mentors offer personalized support, helping you troubleshoot problems, review your code, and navigate more complex topics as your skills develop.
- Join online communities: Engage with other learners and professionals in Kaggle through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
- Build real-world projects: Apply your Kaggle skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using Kaggle in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
- Stay updated: Since Kaggle is continually evolving, staying informed about the latest developments and advanced features is essential. Follow relevant blogs, subscribe to newsletters, and participate in workshops to keep your skills up-to-date and relevant.
How long does it take to learn Kaggle?
The time it takes to learn Kaggle depends greatly on several factors, including your prior experience, the complexity of the language or tech stack, and how much time you dedicate to learning. Here’s a general framework to help you set realistic expectations:
- Beginner level: If you are starting from scratch, getting comfortable with the basics of Kaggle typically takes about 3 to 6 months. During this period, you'll learn the fundamental concepts and begin applying them in simple projects.
- Intermediate level: Advancing to an intermediate level can take an additional 6 to 12 months. At this stage, you should be working on more complex projects and deepening your understanding of Kaggle’s more advanced features and best practices.
- Advanced level: Achieving proficiency or an advanced level of skill in Kaggle generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of Kaggle, contributing to major projects, and possibly specializing in specific areas within Kaggle.
- Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in Kaggle. Engaging with new developments, tools, and methodologies in Kaggle is a continuous process throughout your career.
Setting personal learning goals and maintaining a regular learning schedule are crucial. Consider leveraging resources like Codementor to access personalized mentorship and expert guidance, which can accelerate your learning process and help you tackle specific challenges more efficiently.
How much does it cost to find a Kaggle tutor on Codementor?
The cost of finding a Kaggle tutor on Codementor depends on several factors, including the tutor's experience level, the complexity of the topic, and the length of the mentoring session. Here is a breakdown to help you understand the pricing structure:
- Tutor experience: Tutors with extensive experience or high demand skills in Kaggle typically charge higher rates. Conversely, emerging professionals might offer more affordable pricing.
- Pro plans: Codementor also offers subscription plans that provide full access to all mentors and include features like automated mentor matching, which can be a cost-effective option for regular, ongoing support.
- Project-based pricing: If you have a specific project, mentors may offer a flat rate for the complete task instead of an hourly charge. This range can vary widely depending on the project's scope and complexity.
To find the best rate, browse through our Kaggle tutors’ profiles on Codementor, where you can view their rates and read reviews from other learners. This will help you choose a tutor who fits your budget and learning needs.
What are the benefits of learning Kaggle with a dedicated tutor?
Learning Kaggle with a dedicated tutor from Codementor offers several significant benefits that can accelerate your understanding and proficiency:
- Personalized learning: A dedicated tutor adapts the learning experience to your specific needs, skills, and goals. This personalization ensures that you are not just learning Kaggle, but exceling in a way that directly aligns with your objectives.
- Immediate feedback and assistance: Unlike self-paced online courses, a dedicated tutor provides instant feedback on your code, concepts, and practices. This immediate response helps eliminate misunderstandings and sharpens your skills in real-time, making the learning process more efficient.
- Motivation and accountability: Regular sessions with a tutor keep you motivated and accountable. Learning Kaggle can be challenging, and having a dedicated mentor ensures you stay on track and continue making progress towards your learning goals.
- Access to expert insights: Dedicated tutors often bring years of experience and industry knowledge. They can provide insights into best practices, current trends, and professional advice that are invaluable for both learning and career development.
- Career guidance: Tutors can also offer guidance on how to apply Kaggle in professional settings, assist in building a relevant portfolio, and advise on career opportunities, which is particularly beneficial if you plan to transition into a new role or industry.
By leveraging these benefits, you can significantly improve your competency in Kaggle in a structured, supportive, and effective environment.
How does personalized Kaggle mentoring differ from traditional classroom learning?
Personalized Kaggle mentoring through Codementor offers a unique and effective learning approach compared to traditional classroom learning, particularly in these key aspects:
- Customized content: Personalized mentoring adapts the learning material and pace specifically to your needs and skill level. This means the sessions can focus on areas where you need the most help or interest, unlike classroom settings which follow a fixed curriculum for all students.
- One-on-one attention: With personalized mentoring, you receive the undivided attention of the tutor. This allows for immediate feedback and detailed explanations, ensuring that no questions are left unanswered, and concepts are fully understood.
- Flexible scheduling: Personalized mentoring is arranged around your schedule, providing the flexibility to learn at times that are most convenient for you. This is often not possible in traditional classroom settings, which operate on a fixed schedule.
- Pace of learning: In personalized mentoring, the pace can be adjusted according to how quickly or slowly you grasp new concepts. This custom pacing can significantly enhance the learning experience, as opposed to a classroom environment where the pace is set and may not align with every student’s learning speed.
- Practical, hands-on learning: Mentors can provide more practical, hands-on learning experiences tailored to real-world applications. This direct application of skills is often more limited in classroom settings due to the general nature of the curriculum and the number of students involved.
Personalized mentoring thus provides a more tailored, flexible, and intensive learning experience, making it ideal for those who seek a focused and practical approach to mastering Kaggle.
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