& DEEP LEARNING
Tech Mahindra along with FORE School of Management, New Delhi will prepare the future Big Data Engineers to fulﬁll the market demand.
5 Months -
Total 70 Hrs of Learning
Saturday & Sunday
03:30 Pm - 05:30 Pm
3 Easy EMI
Why to Take
Machine Learning & Deep Learning
Machine Learning (ML) and Deep Learning have been subjects of study since the inception of neural networks. With advancement in technology, especially, Graphical Processing Units (GPUs), the demand for knowledgeable and skilled personnel in this area has received a fillip. Applications of ML & Deep Learning range from Computer Vision to Speech recognition & translation to marketing and to drug discovery. It is one of the fastest growing fields of Artificial Intelligence. The objectives of the present program are:
1) To work on important technologies of ML & AI: Deep Learning, Natural Language Processing and Reinforcement Learning
2) Developing skills in predictive analytics using ML and Deep Learning algorithms
3) Practical implementation of every technique with real world applications
WHO SHOULD ATTEND?
DATA SCIENTISTS/ DEVELOPERS
Lecturers and Professors for extending the horizon of their knowledge through deepening their research skills.
Ambitious Executives (from Private/Public sectors) with 0-2 year of experience looking forward to sharpening their skills in making sense of data in order to innovate and add more value to their organization and to society
Techniques taught to them will have applications in a broad array of disciplines.
Why Education Lanes
for 5 Months (70 Hours)
3 Easy EMI Option
Interactual Learning Platform
24 Hours Support
Bring Education to your Home
We are keenly aware that our participants come from varied backgrounds—both college wise and basic-education-wise. We strongly believe that a course in data analytics can only be practice-based rather than theory based. We also believe that a practice based course requires constant interaction with the teacher during lecture hours in real time. As it is an online course, the teaching pedagogy is like this: First, the theory part is conceptually explained without getting into mathematics and then a project is undertaken to implement the techniques. Data sets for implementation are made available in advance and so a copy of code (or hints on it) that we need to execute. Code is numbered and copiously commented so that long after the lecture has finished, students can go back through the code/comments and refresh their knowledge. During the lecture, we execute this code (or prompt students to fill in the gaps), line-by-line and explain the steps. At his end, the student executes the required code on his laptop. Consequently, results are available at our end as also with the Students immediately. In short, both the teacher and students are working on their respective laptops simultaneously; students solve their problems and ask any questions to clarify. The whole experience is just as if everyone is sitting in a laboratory and working together. Our e-learning platform has a wealth of material and articles reflecting latest developments in this field; it is frequently
5 Months -
71 Hrs of Learning
Introduction to ML, and Data Exploration and Visualization
Natural Language Processing
**Above Modules are sub-divided into further more modules.
Prof. Ashok Kumar Harnal
Prof. Ashok Kumar Harnal: Graduated from IIT Delhi in Electronics and Communication; M. Phil with Distinctionfrom Punjab University, Chandigarh, and MA (Economics) from Punjabi University. Expert in Big Data, DataAnalytics and Deep Learning, both on the technology side as also on Analytics side. Extensively taught faculty andstudents on the subject of big data technology and analytics. Has been associated with University of California,Riverside, US, in one of the Executive Education programs on Big Data and Data Analytics for last three years.Participated in various machine learning projects with real world data in areas of business, environment, marketingand advertisement. Conceived, planned and implemented in Defence Estates three country-wide informationsystems: a) Raksha Bhoomi to computerize land records; b) Knowledge Management of land-title relatedfiles/maps in all Defence Estates offices; and c) Setting up of a Disaster Management organization, Archival Unitand Resource Center, at Delhi and at Pune for safe storage of land-title related records in paper, digital & microfilmforms. Authored two books: one on Programing Games on Computers and the other on Linux Applications andAdministration; both books have been published by Tata McGraw-Hill.
Prof. Lalit K Jiwani, Professor at UCR: PhD, IIT Delhi (Signal Processing) and M.Tech. (Integrated Electronics and Circuits) from Department of Electrical Engineering, IIT Delhi. Experienced academician and researcher having worked both with leading academic institution and technology industry. His primary thrust is in the creation and application of Information Technology for Business and Management. He has teaching and research interest in the area of Digital Signal Processing, Statistics and Random Processes, Machine Learning and Pattern Recognition and Deep Learning, NLP, Image and Video Processing. He is specially interested in the role of technology in business and value creation. He has presented his work in leading conferences of IEEE and European Signal Processing Society in USA, Canada, Denmark, Singapore and India. He was the Session Chair for 2016 IEEE Region 10 Conference (TENCON 2016) Singapore. He is a member of IEEE and IEEE Signal Processing Society.
Prof. Lalit K Jiwani
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