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analyticsindiam · 5 years ago

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Top 10 Courses And Training Programs On Artificial Intelligence In India: Ranking 2019

Analytics India Magazine brings the ranking on AI courses and training programs for the second year in a row. Our annual ranking is a result of extensive research and analysis of various parameters submitted by the institutes. We invited inputs from more than 20 leading institutes offering courses on artificial intelligence. They have been ranked on the basis of four parameters — course content, faculty, student experience and other determining factors such as entry criteria of students and external collaboration. Last year we ranked basis on only 2 parameters which we increased to 4 this year. Each criteria has been ranked on the scale of 0 to 5. See last year’s ranking - 2018 | 2017

1| PG Program in AI and Machine Learning By Great Learning

Year Of Inception: 2013 Cities Of Operation: Mumbai, Bengaluru, Delhi/NCR, Chennai, Hyderabad, Pune, Online variant across all cities Duration Of The Program: 12 months Total Number Of Learning Hours: 400+ Learning Hours in Blended Mode and 240+ hours in Online Mentorship Mode Course Fees: ₹ 2,15,000 for online, ₹ 3,25,000 for classroom Offering courses in areas such as analytics, data science, machine learning, AI and others, it is taken by thousands of professionals every year. Great Learning has impacted more than 13000 professionals delivering 8+ million hours of learning in analytics, data science, AI and machine learning. They have an alumni network of 10000+ professionals working across marquee organisations in India and beyond. Parameter 1: Course Content (4.8) Comprehensiveness: The program builds a solid foundation of AI and ML techniques while focusing on areas such as computer vision, NLP, recommendation systems, anomaly detection and more. It also covers a range of topics from statistical techniques to traditional supervised and unsupervised learning methods such as reinforcement learning, GANs etc. The program covers 12+ hands-on projects and a capstone project. It also provides guidance from industry experts, hackathons and interactive lab sessions. Capstone Projects/Internships: More than 3 months Frequency Of Course Updation: Frequently: Every new batch Assessing Students At The End Of The Course: Students need to complete lab assessments with 60%+ marks, capstone project with 60%+ marks and no more than 2 courses should be incomplete at the end of the program. They are also accessed through quizzes and the ability to apply what they have learned through labs and projects. Learning Resources: Videos & Downloadable Resources, LMS & Online webinars, Organize industry guest lectures, Analytics/Data Science events in-campus, Hands-on coding and projects Parameter 2: Faculty (4.6) Total Number Of Faculty Members: 200 Student To Faculty Ratio: 10:1 for the online version, 45:1 for classroom Parameter 3: Student Experience (4.8) Percentage Of Students Who Complete The Course: 93% Post-completion Engagement: Learners are regularly updated on the relevant jobs posting by their hiring partners. Participants can also attend career fairs organised by Great Learning. They also offer mentorship roles. Placement Assistance: Assistance through an alumni network, resume designing and sharing with job consultants, career counselling and mock interviews, Access to GL Excelerate Career Fairs, internal job boards, shareable e-portfolio and more. Parameter 4: Other Determining Factors (4.8) Entry Criteria Of Students: Bachelor's degree with a minimum of 50% aggregate marks or equivalent. Proficiency in at least one programming language and familiarity with college-level mathematics and statistics External Collaboration: Great Lakes collaborates with UT Austin-McCombs IIT-Bombay, as well as other practising data scientists and AI experts. Upon completion, all successful participants get a dual certificate from Great Lakes and The University of Texas at Austin. The overall rating is 4.75.

2| PG Diploma in Machine Learning and AI from IIIT-B and upGrad

Year Of Inception: Mumbai 2015 Cities Of Operation: Mumbai, Bengaluru, Delhi/NCR, Chennai, Hyderabad, Kolkata, Pune, Jaipur, Pan India Name Of The AI Program: Duration Of The Program: 11 months Total Number Of Learning Hours: 400 hours Course Fees: ₹ 2,85,000 Founded by media stalwart Ronnie Screwvala, upGrad provides rigorous industry-relevant programs designed and delivered in collaboration with renowned faculty and industry. It has partnered with 50+ companies like Star TV, Disney, Uber, Google, Microsoft, BookMyShow, etc. and provides the latest technology, pedagogy and services to learners. Parameter 1: Course Content (4.8) Comprehensiveness: Lectures are delivered completely online, 90% through asynchronous videos by industry experts and leading faculty to ensure flexibility for Learners. About 10% of learner time is spent attending live lectures by faculty & industry experts and clarifying academic doubts. In addition, Hackathons are organised where students get a chance to work on challenging industry problems and real data sets. Capstone Projects/Internships: None Frequency Of Course Updation: Regularly- Every Year Assessing Students At The End Of The Course: The course includes graded questions in every module in the form of MCQs, coding questions and open text questions. At the end of every course, students attempt a proctored online exam. Each course ends with an industry-sourced capstone project, the performance of which contributes to the overall GPA. Also, at the end of every 2 months, learners attempt an interview-skill-assessment test. While these are not linked to the GPA, it gives learners timely status on where they stand w.r.t industry requirements. Learning Resources: Videos & Downloadable Resources, LMS & Online webinars, Organize industry guest lectures, Analytics/Data Science events in-campus, Hands-on coding and projects, Access to IIIT-B Library Parameter 2: Faculty (4.6) Total Number Of Faculty Members: 85 Parameter 3: Student Experience (4.7) Percentage Of Students Who Complete The Course: 95% Post-completion Engagement: It provides 360-degree career support and career assistance is provided by both IIIT and UpGrad. They also provide 1-1 student mentorship and peer to peer interaction with offline basecamps. Placement Assistance: Assistance through an alumni network, Career counselling and mock interviews Parameter 4: Other Determining Factors (4.8) Entry Criteria Of Students: Bachelor's Degree with a minimum one year of work experience or a degree in Mathematics or Statistics External Collaboration: IIIT - Bangalore The overall rating is 4.72.

3| Advanced Certification in Artificial Intelligence and Machine Learning By International Institute of Information Technology, Hyderabad and TalentSprint

Year Of Inception: IIIT-Hyderabad, 1998; TalentSprint 2009 Cities Of Operation: Bengaluru, Hyderabad Duration Of The Program: Classroom format: 13 weeks; Hybrid format (classroom+online): 18 weeks; Online classes: 15 weeks Total Number Of Learning Hours: 120 hours Course Fees: ₹ 2,00,000 + taxes (special scholarships for women and young professionals) Kohli Center on Intelligent Systems (KCIS) at IIIT-H was established in 2015 which is a highly rated machine learning lab in India. TalentSprint brings high-end and deep-tech education to aspiring and experienced professionals. It partners with world-class academic institutions like IIIT Hyderabad, IIM Kolkata, IIT Hyderabad and global corporations like Google and Pegasystems to develop and offer disruptive programs. Parameter 1: Course Content (4.8) Comprehensiveness: The Program curriculum is designed to solve real-world problems. From refreshing your coding knowledge to translating real-world problems in terms of AI/ML abstractions and creating practical applications, the comprehensive nature of the program covers all the important aspects. Capstone Projects/Internships: Less than 3 months Frequency Of Course Updation: Frequently- Every new batch Assessing Students At The End Of The Course: The program is designed to have continuous formative and summative assessments. The final scores are determined based on the sum of scores of various assessments. Learning Resources: Videos & Downloadable Resources, LMS & Online webinars, Organize industry guest lectures, Analytics/Data Science events in-campus, Hands-on coding and projects, Hackathons, Mentor Support, One-on-one mentoring sessions Parameter 2: Faculty (4.6) Total Number Of Faculty Members: 9 Student To Faculty Ratio: 20:1 Parameter 3: Student Experience (4.6) Percentage Of Students Who Complete The Course: 93% Post-completion Engagement: Access to learning the material through LMS which is provided for a year since the beginning of the program. The participants receive executive alumni status from IIIT Hyderabad giving them access to exclusive events like guest lectures and industry talks. Placement Assistance: Assistance through an alumni network, sharing potential hiring opportunities with the alumni network. Parameter 4: Other Determining Factors (4.6) Entry Criteria Of Students: Coding experience required External Collaboration: None The overall rating is 4.65.

4| Full Stack Machine Learning and AI Program By Jigsaw Academy

Year Of Inception: 2011 Cities Of Operation: Bengaluru, Delhi/NCR, Hyderabad, Online Name Of The AI Program: Duration Of The Program: 6 months Total Number Of Learning Hours: 12+ hours Course Fees: ₹ 48,400 + taxes Jigsaw Academy provides training in data and analytics, and believe that it can change the way not just industries but individuals work. It is their constant endeavour to improve how these skills are taught and this is what distinguishes Jigsaw from other training providers. Parameter 1: Course Content (4.7) Comprehensiveness: The program focuses on building a solid foundation. It gives additional access to IBM’s ML platforms and Watson via the data science experience and content. It gives exposure to Python, Keras, TensorFlow and more. Capstone Projects/Internships: More than 3 months Frequency Of Course Updation: Frequently- Every new batch Assessing Students At The End Of The Course: There is multiple assessments throughout the course, as well as the capstone and final viva voce. Learning Resources: Videos & Downloadable Resources, LMS & Online webinars, Organize industry guest lectures, Analytics/Data Science events in-campus, Hands-on coding and projects Parameter 2: Faculty (4.6) Total Number Of Faculty Members: 10 Student To Faculty Ratio: 4:1 Parameter 3: Student Experience (4.0) Percentage Of Students Who Complete The Course: 98% Post-completion Engagement: Invitation to guest lectures, webinars. Assistance through an alumni network, event updates. Placement Assistance: Assistance through an alumni network, Career counselling and mock interviews Parameter 4: Other Determining Factors (4.2) Entry Criteria Of Students: No criteria External Collaboration: The course content and use cases have been designed with extensive input from industry subject matter experts from several domains, and additional input via Jigsaw’s corporate and academic collaborations. The overall rating is 4.37.

5| Applied AI and Machine Learning Specialization, AI & Deep Learning with Python By Analytixlabs

Year Of Inception: 2011 Cities Of Operation: Bengaluru, Delhi/NCR, Global through instructor-led online and e-learning mode Name Of The AI Program: Duration Of The Program: Six months Total Number Of Learning Hours: 280 Course Fees: ₹ 25,000 + taxes for 4 months duration,₹ 48,000 + taxes for 6 months duration A pioneer in edutech industry, it has been providing high-quality training since 2011 in fields of applied AI and machine learning. They have a team led by the team of McKinsey, IIM, ISB, FMS and IIT alumni with rich industry experience. Parameter 1: Course Content (4.2) Comprehensiveness: Course modules and case studies are aligned and tailor-made to develop skills sets for corporate projects. The training and pedagogy are validated by seasoned professionals for their suitability and value addition. Capstone Projects/Internships: More than 3 months Frequency Of Course Updation: Frequently- Every new batch Assessing Students At The End Of The Course: Their assessment strategies include analysing the case studies, personal interview sessions with experts. They have internal assessment tool designed to test students at the conceptual and practical level. Parameter 2: Faculty (4.3) Total Number Of Faculty Members: 17 Student To Faculty Ratio: 30:1 Parameter 3: Student Experience (4.4) Percentage Of Students Who Complete The Course: Over 85% Post-completion Engagement: They help in profile building and career guidance. Learners also have access to their online learning management system for future references. Placement Assistance: Assistance through an alumni network, resume designing and sharing with job consultants, mock interviews, We believe in quality training and support all throughout the span of the course and beyond. Parameter 4: Other Determining Factors (4.4) Entry Criteria Of Students: Students are selected based on STEM or Non-STEM and work experience (if any) and the suitable course is suggested accordingly. External Collaboration: They have a partnership with Asean Data Analytics Exchange, Malaysia. This collaboration is recognized with the HR Ministry of Malaysia and we have already started delivery of programs there. The overall rating is 4.32.

6| Artificial Intelligence Engineer Master's Program By Simplilearn

Year Of Inception: 2010 Cities Of Operation: Mumbai, Bengaluru, Delhi/NCR, Chennai, Hyderabad, Pune Duration Of The Program: 1 year Total Number Of Learning Hours: 400+ Course Fees: ₹ 44,999 Simplilearn provides outcome-based online training across digital technologies and applications such as big data, machine learning, AI, cloud computing, cybersecurity, digital marketing and other emerging technologies. Based in San Francisco, CA, Raleigh, NC and Bangalore, India; Simplilearn has helped more than one million professionals and 1,000 companies across 150 countries get trained, acquire certifications and reach their business and career goals. Parameter 1: Course Content (4.3) Comprehensiveness: This course is co-developed with IBM and gives training on skills such as deep learning, machine learning and programming languages. Some of the techniques covered are AI-based supervised and unsupervised techniques like regression, Multinomial Naïve Bayes, SVM, tree-based algorithms, NLP etc. Capstone Projects/Internships: Less than 3 months Frequency Of Course Updation: Regularly: Every Year Accessing Students At The End Of Course: Apart from the attendance, they also assess student’s performance on the basis of Capstone projects. Learning Resources: Videos & Downloadable Resources, LMS & Online webinars, Organize industry guest lectures, Analytics/Data Science events in-campus, Hands-on coding and projects, Self-paced videos and Live Virtual classes, integrated labs Parameter 2: Faculty (4.3) Total Number Of Faculty Members: 35 Student To Faculty Ratio: 35:1 Parameter 3: Student Experience (4.3) Percentage Of Students Who Complete The Course: Over 70 percent Post-completion Engagement: They have a team that connects with learners to understand the impact the program has created for them. They also have a JobAssist program, an India specific offering in Partnership with IIMJobs.com to help the learners land their dream job. Upon successful completion of the Master’s Program, the learner will be eligible to apply for this program and details will be shared with IIMJobs. Placement Assistance: Resume designing and sharing with Job consultants, Career counselling and mock interviews, IIMJobs Pro-Membership for 6 Months, Career Fairs Parameter 4: Other Determining Factors (4.0) Entry Criteria Of Students: Anyone with a technical background can apply External Collaboration: Simplilearn has partnered with IBM that combines Simplilearn’s seamless training experience and world-class instructors with IBM’s state-of-the-art labs and course content. Upon completion of this Master's Program, the learner receives certificates from IBM for IBM specific courses and Simplilearn for the AI courses on the learning path. The overall rating is 4.22.

7| Post-Graduate Diploma in Machine Learning and Artificial Intelligence By Careers of Tomorrow - Amity University Online

Year Of Inception: 2018 Cities Of Operation: Mumbai, Bengaluru, Delhi/NCR, Chennai, Hyderabad Duration Of The Program: 12 Months Total Number Of Learning Hours: 400+ Hours Course Fees: ₹ 1,55,000 Careers of Tomorrow by Amity University Online is an online education platform, where they also have programs for data science, blockchain technology, machine learning, cybersecurity and others apart from the usual courses. Parameter 1: Course Content (4.3) Comprehensiveness: The course offers machine learning, reinforcement learning and deep learning all in a single program. The curriculum and pedagogy were decided by a panel of veterans from academia and industry to make it best suited for the industry. It covers extensive hands-on and tool-based training on various tools and libraries like Anaconda, Jupyter Notebooks, Tableau, Tensor Flow, Keras, neural networks etc. Capstone Projects/Internships: Less than 3 months Frequency Of Course Updation: Frequently- Every new batch Assessing Students At The End Of The Course: Every module has quizzes, assignments, hands-on, case studies and examination. The examination is conducted semester-wise at the end of each module. Learning Resources: Books, Videos & Downloadable Resources, LMS & Online webinars, Organize industry guest lectures, Analytics/Data Science events in-campus, Hands-on coding and projects, 24*7 Assistance by Tutor Associates is also provided

Parameter 2: Faculty (4.5)

Total Number Of Faculty Members: 5 Student To Faculty Ratio: 20:1 Parameter 3: Student Experience (4.1) Percentage Of Students Who Complete The Course: 100% Post-completion Engagement: Students support, newsletter, membership, events, alumni activities, career assistance for up to 12 months. Placement Assistance: Guaranteed job interview with hiring partner, assistance through an alumni network, resume designing and sharing with job consultants, career counselling and mock interviews, sponsor students to attend industry event/conference to network for industry peers Parameter 4: Other Determining Factors (3.9) Entry Criteria Of Students: Interview by Admission counsellors and Program Director External Collaboration: None The overall rating is 4.20.

8| Post-Graduate Program in Artificial Intelligence and Machine Learning By Edureka

Vineet Chaturvedi, Co-founder, Edureka Year Of Inception: 2011 Cities Of Operation: Bengaluru, New Delhi & international reach across 100+ countries through live, online, instructor-led and self-paced programs Duration Of The Program: 9 Months Total Number Of Learning Hours: 450+ Course Fees: ₹2,62,500 (Inclusive of all taxes) Edureka is a global e-learning platform with 200+ courses in trending technologies. They are pioneers of the live, instructor-led learning model, and offer short and long-term courses supported by online resources and 24x7 lifetime support. They boast of a learner community across 100+ countries. Parameter 1: Course Content (4.1) Comprehensiveness: Skills covered in the course are predictive analytics, practical applications of AI and ML, graphical models and reinforcement learning, deep learning, Python, TensorFlow, Keras, and OpenAI Gym. Individuals are mentored by NITW professors and on course completion, the individual receives a PGP certificate and become an alumnus of the E&ICT Academy, NITW. Capstone Projects/Internships: Less than 3 months Frequency Of Course Updation: Regularly- Every Year Accessing Students At The End Of Course: Assessment is done on multiple levels. Level 1 includes tests, quizzes. Level 2 consists of a live, industry-standard project and level 3 comprises of the final test and a capstone project after completion of the program. Learning Resources: LMS, Recorded Video Sessions, Class Recordings, PDFs, Presentations, Mobile App, Downloadable Content, Blogs, YouTube Videos, and Community of Experts for Discussions Parameter 2: Faculty (4.4) Total Number Of Faculty Members: 15 Student To Faculty Ratio: 15:1 Parameter 3: Student Experience (4.1) Post-completion Engagement: Placement assistance, alumni network of NITW and Edureka and community Placement Assistance: Resume building, interview preparation, mock interviews, access to the alumni network, community discussions, floating of job openings in corporate clients and partner companies in learner groups, referring relevant learners to corporate partners as and when requirements arise Parameter 4: Other Determining Factors (4.1) Entry Criteria Of Students: Based on merit & eligibility determined through an interview External Collaboration: It has corporate partners such as Cisco, TCS, Infosys, VISA, Wipro, KPMG etc. and educational institutes to promote post-graduate IT training such as with NIT, Warangal and Rourkela and IIT, Guwahati. The overall rating is 4.17.

9| Applied Machine Learning Course By Applied AI Course

Team Applied AI Year Of Inception: 2017 Cities Of Operation: Mumbai, Bengaluru, Delhi/NCR, Chennai, Hyderabad, Kolkata, Pune, Jaipur, Worldwide (Online) Name Of The AI Program: Duration Of The Program: 12 Months (Self-paced) Total Number Of Learning Hours: 150+ Hours video content and 70+ Hours Live sessions Course Fees: ₹ 25,000 + taxes Applied AI Course is an ed-tech company that runs an online program on Machine Learning and Artificial Intelligence for engineering grads and entry-level & experienced techies. Parameter 1: Course Content (4.1) Comprehensiveness: The course helps participants get a hang on real-world business problems. The program balances theory and practises while giving more preference to practical and applied aspects of AI. Capstone Projects/Internships: More than 3 months Frequency Of Course Updation: Frequently: Every new batch Assessing Students At The End Of The Course: Regular assessment tests are done using an AI assessment platform developed by the Applied AI team. Learning Resources: Books, Videos & Downloadable Resources, LMS & Online webinars, Organize industry guest lectures, Analytics/Data Science events in-campus, Hands-on coding and projects Parameter 2: Faculty (3.9) Total Number Of Faculty Members: 16 Student To Faculty Ratio: 50:1 Parameter 3: Student Experience (3.8) Percentage Of Students Who Complete The Course: 25 to 30 Post-completion Engagement: Resume, portfolio building and interview preparation through mock interviews. Placement Assistance: Guaranteed job placement with the corporate, guaranteed job interview with hiring partner, career counselling and mock interviews Parameter 4: Other Determining Factors (3.9) External Collaboration: They have delivered up-skilling training programs to enterprises and educational institutions such as Deutsche Telekom, SPI Global, CA technologies, ACE engineering college among others. The overall rating is 3.92.

10| Artificial Intelligence Foundation Program By Pearson Professional

Instructors Noah Gift, Jared P Lander, Ben Forta (L-R) Year Of Inception: 1844 Cities Of Operation: Mumbai, Bengaluru, Delhi/NCR, Chennai, Hyderabad, Kolkata, Pune, Jaipur Duration Of The Program: 6 Months Total Number Of Learning Hours: 58 Hours Course Fees: ₹ 9,971 One of the largest education companies with over 35,000 employees in more than 70 countries, it provides education in many domains. The AI curriculum is set by Nasscom and Sector Skills Council of India. Pearson’s Artificial Intelligence Foundation curriculum is created and taught by globally renowned experts in the field of AI ensuring world-class content. Parameter 1: Course Content (3.7) Comprehensiveness: The self-paced videos are complemented by virtual labs which provide extensive hands-on coding practice within the course environment. It covers topics such as fundamentals of databases, object-oriented programming, data structures, programming languages, regressions, different forms of graphs, etc. Capstone Projects/Internships: None Frequency Of Course Updation: Regularly: Every Year Assessing Students At The End Of The Course: Students are assessed through regular MCQs and coding problems. In total 28 coding problems and 10 MCQ type assessments are present in the course Learning Resources: Videos & Downloadable Resources, Hands-on coding and projects Parameter 2: Faculty (3.5) Total Number Of Faculty Members: 4 Parameter 3: Student Experience (3.2) Parameter 4: Other Determining Factors (3.7) The overall rating is 3.52. Read the full article

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theresawelchy · 6 years ago

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Hackathon Winner Interview: Penn State | Kaggle University Club

We believe today’s university students are tomorrow’s leading data scientists. As such, we decided to launch Kaggle University Club — a virtual community and Slack channel for existing data science clubs who want to compete in Kaggle competitions together. As our end-of-year event, we hosted our first-ever University Hackathon!

18 total kernels were submitted and the three top scoring teams won exclusive Kaggle swag and an opportunity to be featured here, on No Free Hunch. Please enjoy this profile from one of the top scoring university teams, ‘Team NDL’ from Penn State!

To read more about the Hackathon and its grading criteria, see here: Winter ‘18 Hackathon and to read this team’s winning kernel, visit: Team NDL: Algorithms and Illnesses.

MEET THE STUDENTS

Neil Ashtekar

Major: Computer Science Hometown: State College, Pennsylvania Anticipated graduation: Spring 2020

What brought you to data science?

I had read a lot about machine learning/artificial intelligence in the news, and I wanted to see what all the hype was about. So, I decided to complete Andrew Ng’s machine learning class on Coursera. I learned a ton, and I really enjoyed the material. After finishing the class, I wanted to apply what I learned, so I turned to Kaggle. I started out with the basic competitions (Titanic, MNIST), then moved on to work with some more interesting datasets (Kobe Bryant Shot Selection, World Happiness Predictors).

What are your career aspirations after graduation?

I want to get a job as a Machine Learning Engineer (not sure where!).

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William Wright

Major: Mathematics

Hometown: Dallas, Texas Anticipated graduation date: Spring 2019

What brought you to data science? I originally wanted to become a math professor, but after reading Smart People Should Build Things by Andrew Yang and Zero to One by Peter Thiel, I became more interested in pursuing a career involving technology. In his book, Yang claims the decisions we make in the next decade will decide whether society moves towards the future of Mad Max or Star Trek. This comment really stuck with me and inspired me to start learning python and to join Nittany Data Labs (the Penn State data science club).

What are your career aspirations after graduation?

I’d love to work as a data scientist or machine learning engineer.

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Izzi Oakes

Major: Integrative Arts

Anticipated graduation date: Fall 2020

What brought you to data science?

I went to my university’s first data science club meeting by random chance, and within five minutes I was hooked. This was about a year ago, and I had never programmed anything before and was in a completely unrelated major. I’ve spent the past year grabbing any and all resources online I could find related to data science and devouring them, as well as moving towards studying higher level math and statistics.

What are your career aspirations after graduation?

I’d like to be in a position where I do work related to some kind of intersection between machine learning and music / visual arts.

TEAM QUESTIONS

How familiar was your team with Kaggle competitions prior to the Hackathon?

A few of us had completed Kaggle competitions in the past, but they were mainly the beginner ones. This was our first time working on a competition as a team, as well as on a longer term project, as this competition lasted about a month.

How did your team work together on your Kernel?

We started out working individually to explore and understand the data. After a week of exploration on our own, we met up to talk about our findings and ideas moving forward. At this point, we created a shared kernel and implemented our ideas in code.

What was the most challenging part of the hackathon for you?

Working with text data! None of us had any experience with natural language processing, so understanding how to represent the written review data was challenging.

What surprised you most about the competition?

We were surprised by how well a very simple linear regression model worked with the problem. We had a long conversation about whether we should be using Neural Networks to solve the problem, and potentially why other approaches would work just as well.

What advice would you give another student who wanted to compete in a Kaggle competition or even a hackathon?

If you’re just starting, definitely start with one of the beginner challenges. Try to work your way through it as much as you can by googling things if you get stuck, then begin looking through existing kernels people have once you’re finished. These will give you great approaches to the problem, and you can begin on improving your own model.

Also, try to build your way up to this if you’re just starting. If you really don’t feel like you’re understanding anything you’re doing, there are many great free ML courses and books online!

Anything else?

Thanks a lot for featuring us!

You’re welcome!

Team NDL from Penn State University (from left to right: Neil Ashtekar, Izzi Oakes, Suraj Dalsania, Will Wright, Ming Ju Li).

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