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Tanisha Bhayani
NumPy tutor
US$50.00 /15 min6 years of experience in building and managing AI Systems. * I develop end-to-end AI systems from requirements analysis, and data gathering to deployment, implementing new methods/research papers, and turning projects into research outcomes. I have achieved substantial performance in DL/ML/RL models for CV and NLP domain problems. * Implementing and deploying projects handling and maintaining scalability, research papers as well as POCs. * Project planning, requirements gathering, and analysing requirements to define the system's architecture, and implementation timeline. * Provide mentoring to junior developers for ML projects. Website: [roshantanisha.github.io/?ref=codementor](http://roshantanisha.github.io/?ref=codementor) Summary: I like to work on technology that is smart, simple and sophisticated. This sums up the vast knowledge required to work on projects to excel in a working product. I like to train Deep Neural Networks and understand them well. I have mentored many students for their AI careers, teaching them Machine Learning and Mathematics. I am a mentor for the RFS (Reach for the Stars) Programme by the Aga Khan Education Board for India. I am an alumnus of this program as well. I have a cumulative experience of 6 years working in the product and service-based industry for creating Machine Learning projects. I have done some innovative work that I am proud of and am continuing to do so. I try my best to contribute my expertise to the project I am working on. Highly Experienced in Machine Learning, Deep Learning, Advanced Deep Learning, Artificial Intelligence, and Algorithms, including models in the production environment, and deploying ML models. Working with top Indian colleges like BITS, niche NLP and CV, real-estate startups, MNCs, and Fortune top 20 companies, working with sensitive anonymized datasets, and creating state-of-the-art models are some of my achievements. I have strong and correct knowledge of Deep Learning concepts from the above experiences.
- NumPy
- Python 3
- AI
- Machine learning
- Machine Learning Deployment
- Web Development
- Keras
- TensorFlow
- SQL
- Python
- Deep Learning
- TensorFlow.js
- Angular
- React
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- Web Scraping
- Scrapy
- TypeScript
- Responsive Web Development
- MongoDB
- PostgreSQL
- MySQL
- Computer Vision Engineering
- PyTorch
- Deep Learning Pipeline
- Deep Reinforcement Learning
- Scipy
- Matplotlib
- Pandas
- Python 2
- Cloud Functions
- AWS SageMaker
- Azure
- Google Cloud Platform
- GPU
- AWS
- Kubernetes
- Docker
- Machine Learning System Design
- Machine Learning Frameworks
- Machine Learning Research
- Machine Learning Models
- Open Source Software
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- GPT-4
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- GPT
- Research
- LLM
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- Data Visualization
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- Data Science
- Artificial Neural Networks
- Flask
Gopal Chitalia
NumPy 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.
Kazi Sohan
NumPy tutor
US$20.00 /15 minI love to teach what I know. I think I can explain things easily with examples. I have been working as a Software Engineer for the last 5 years and have worked with some of the world's most reputed companies like **Open AI, Scale AI, Doloras Lab**, etc as a freelancer.
Belal Haikal
NumPy tutor
US$25.00 /15 minWelcome to visit! I am a full-stack developer with more than 10 years experiences of development. I am good at the following: - React.js, Angular, Vue web development - React Native, Flutter mobile app development - Node.js, Express, JavaScript, - Python, Machine Learning, Generative AI - Swift/SwiftUI/Objective C, Kotlin, Java - SQL, MongoDB, PostgreSQL, Redis - C/C++, C# Thanks
Tushar Gupta
NumPy 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
Vivek Mogalla
NumPy tutor
US$20.00 /15 minHi, Welcome to my profile! I have been in the software development industry for 4 years. My key skills are 1. Python. 2. Django Framework. 3. GitHub (version control tool). 4. Front-end technologies (HTML, CSS, Javascript, Jquery, Ajax) 5. Relational Database (PostgreSQL, MySQL) 6. Deployment Skills I built web applications and Python projects based on the Django framework and provided optimum solutions by solving client problems. Let me find the solutions to your problems too. I am also a mentor. I love teaching Python programming from basic to advanced level and have taught some students in other teaching platforms. I prepare the customized syllabus as per the requirements and experience.
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Users love our NumPy tutors
See the power of our NumPy tutors through glowing user reviews that showcase their successful NumPy learning journeys. Don't miss out on top-notch NumPy training.
“Dejan was very clear in his thinking and problem solving skills when going through an algorithm. Would highly recommend“
hillbilly991 / Jan 2024
Dejan B.
NumPy tutor
“Takes the time to listen to your needs throughout the session rather than brute force teaching a concept. Adaptable, intelligent, great communication skills. Highly recommend.“
Thomas Quaid / Jul 2023
Zack Plauché
NumPy tutor
“Thank you for the quick reply and help with understanding how to combine and process large data. Was great experience collaborating“
Ildana Ruzybayeva / Mar 2023
Moeed Shaik
NumPy tutor
“Smooth and Clear. I asked him to explain to me what he did for the work, and he delivered what I asked for. Very nice and fluent English, Clear mic and voice. He is very knowledgeable about ML, python and will answer your questions quickly.“
The Wise one / Feb 2023
Abdulhakeem Omotolani Yaqoob
NumPy tutor
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Frequently asked questions
How to learn NumPy?
Learning NumPy 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 NumPy. 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 NumPy, 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 NumPy 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 NumPy through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
- Build real-world projects: Apply your NumPy skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using NumPy in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
- Stay updated: Since NumPy 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 NumPy?
The time it takes to learn NumPy 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 NumPy 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 NumPy’s more advanced features and best practices.
- Advanced level: Achieving proficiency or an advanced level of skill in NumPy generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of NumPy, contributing to major projects, and possibly specializing in specific areas within NumPy.
- Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in NumPy. Engaging with new developments, tools, and methodologies in NumPy 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 NumPy tutor on Codementor?
The cost of finding a NumPy 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 NumPy 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 NumPy 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 NumPy with a dedicated tutor?
Learning NumPy 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 NumPy, 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 NumPy 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 NumPy 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 NumPy in a structured, supportive, and effective environment.
How does personalized NumPy mentoring differ from traditional classroom learning?
Personalized NumPy 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 NumPy.
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