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In-Depth Advanced Modules
Hands-on Projects
Career Mentoring
Hours of Live Classes
Placement Partners
Assignments
Exclusive Offers!
Certification Validity
Job Assistance
Machine Learning is one of the hottest career choices today. It is one of the fastest-growing tech employment areas with jobs created far outnumbering the talent pool available.
According to Gartner, 2.3 million Machine Learning Jobs will be generated by 2020. Indeed job trends report also reveals that in terms of most in-demand, AI jobs, Machine Learning Engineer tops the chart with 29.10% increase in job postings.
Today, every industry is going gaga after Artificial Intelligence. This makes it ideal to take up a Machine Learning Course.
By bringing better career opportunities, Online Machine Learning Courses have become the shining star of the moment.
– People with knowledge of Python Programming
– Candidates with an understanding of Statistics, Algebra & Calculus
13 Modules
15+ Hrs of Hands-on Assignments
Well researched assignments have the potential to take the participants on an exciting journey to execute their learnings. That’s our mantra at Digital Vidya.
Each assignment of Digital Vidya’s Machine Learning Course is designed with a focus to provide the best practical experience. Our module assignments to learn Machine Learning focus on enhancing the confidence of our participants.
Our Assignments are close to the actual occurrences in the industry out there. These assignments will be a propeller to helping you learn Machine Learning practically.
Capstone Projects on Offer
To learn Machine learning in the best possible and hands-on method, Digital Vidya’s Machine Learning Course comes with best in class capstone Projects. At the end of each batch, we hold a Capstone Project competition that is open for our students. Successful participants win prizes and recommendations from their lead trainers.
Project Description:
This is one of the most applied areas for AI, Data Science, and Machine Learning across domains and industries. The real world is filled with mostly messy text data, and handling text is an important step towards making smarter algorithms. Using IMDB dataset from the movie domain, the learner will apply the most common concepts of NLP.
Key Takeaway:
This project will empower the learners to build intermediate skills in the natural language processing domain. A few of the fundamentals of working with textual data covered in this project are:
Project Description:
Electroencephalography (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain. For this project, we will use the large EEG database at UCI Machine learning repository. This data arises from a large study to examine EEG correlates of genetic predisposition to alcoholism. One fascinating question is whether the patterns are different for an alcoholic and regular subject?
Key Takeaway:
This capstone project focuses on EEG data analysis, giving an opportunity for students to learn through complexities in dealing with such complex real-world data. The project contains the following exercises:
Project Description:
The banking industry is working in a very competitive environment and needs to strategize to grow its business. This project is related to the marketing campaigns related to term deposits, making an interesting multi-disciplinary work that mixes both the finance and the marketing domain.
Key Takeaway:
The approach to this project is to think, define, design, code, test and tune your solution, in such a way that you apply all aspects of the data science process. The data is a real-world data with unclean and null values.
The objective is to:
Project Description:
E-Commerce has experienced considerable growth since the dawn of the internet as a commercial enterprise. Deep Learning excels at identifying patterns in unstructured data and can predict the class of an uploaded image applied on eCommerce context. This project is an attempt to replicate virtual store assistance through image recognition over an eCommerce Fashion MNIST dataset.
Key Takeaway:
This project focuses on the implementation of Neural Networks to solve complex unstructured data problems. The objective is to: