- Opening Session
- Challenges for a Data Scientist in Modern Age
- New Approach to Building ML Models
- My Next Approach - AutoML
- A Deciding Factor
- Classical Approach - GOFAI
- ANN Approach
- DNN & Pre-trained Models
- Summarising
What you'll learn
- Become a professional data scientist in the modern age
- Learn modern data scientists approach to model building
- Learn how to develop models based on various data types
- Know more about GOFAI, ANN, Transfer Learning and Clustering
Description
Due to technogical innovations in last one or two, the whole outlook of developing AI applications has totally changed. Today, I have several approaches to create an AI solution. A data scientist has to select the best approach to succeed in this area. This course provides a consolidated view of all various options and guides you in developing ML applications in the modern age. Towards the end of the course, I will provide you a cheatsheet that gives you a visual representation of different workflows in the ML development paths and helps in you taking the right decision in selecting an appropriate path for your new data science project.
The course covers various options like MLaaS, Machine Learning as a Service, AutoML, traditional ML development - GOFAI, the newer DNN approach and of course also the Transfer Learning. The cheatsheet helps you to select the option. Each option has its own merits and demerits. In my lectures, I will discuss these, which will make it easier for you in selecting your path. Note that, taking a wrong path, would just result in a criminal wastage of your resources and time. So, it is better that you should first get to know the different options of ML development and the intricacies in each. For each option, I have described what all you need to know or to learn. This course would surely take you on the path of becoming a data scientist of the modern era.
Welcome to the new world of Data Science.
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About the instructors
- 4.53 Calificación
- 3809 Estudiantes
- 4 Cursos
Prof Poornachandra Sarang, Ph.D.
Practicing Data Scientist & Researcher
Poornachandra Sarang, with his 30+ years of IT tenure, possesses a pleasant blend of education and industry experience. He has been a consultant to various top-notch IT companies. He has been a professor of Computer Engineering at the University of Notre Dame and University of Mumbai. He is a Ph.D. advisor in Computer Science, a member of Thesis Advisory committee for students pursuing Ph.D. in Computer Engineering, a course-curriculum setter for undergraduate/graduate courses in Computer Science/Engineering. He has delivered several presentations and keynotes in International conferences across the globe.
Student feedback
Course Rating
Reviews
I recently took this course on modern data science, and I must say, it was fantastic! It really helped me understand all the different ways we can use AI nowadays, like MLaaS and AutoML. The best part was the cheatsheet they provided, which made it so much easier to see how each method works. Plus, they explained everything in a really clear way, so I could easily weigh the pros and cons of each approach. If you're interested in data science and want to build a strong foundation, I highly recommend this course!
The explanations provided are clear and beginner-friendly, making it much easier for me to grasp the fundamental concepts, distinctions, and determining factors.
The course was very well structured and provided a comprehensive understanding of the core concepts of data science. The instructor was very knowledgeable and had a great way of explaining difficult concepts in an easy to understand manner.The course materials were also top-notch. The lectures were well-organized and the slides were easy to follow. Overall, I would highly recommend this data science course to anyone interested in learning about this field. The course provided a solid foundation in data science and gave us the necessary skills to start working with data in a meaningful way. Thanks to this course, I feel confident in my ability to tackle data-driven projects in the future.