Program Overview - Artificial Intelligence and Machine Learning
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Machine Learning (ML) and Artificial Intelligence (AI) is the construction of algorithms that learn from and respond to large datasets faster and make effective predictions
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Today, ML & AI are the #1 skill in-demand globally, with a 3977% growth since 2015
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The program offers professional training, allowing participants to sense how Emerging Technologies, like ML & AI, are enabling new business models, and reshaping the way the economy and businesses operate.
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The program also offers opportunity to work on live industry projects, allowing professionals to study and gain real-world skills
Why Machine Learning & Artificial Intelligence?
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ML & AI’s potential to deliver real-time optimization across industries is just starting to evolve and is set to accelerate and offer greater opportunities over the next three years
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The spending on ML and AI is set to urge towards $47 Billion by 2020 globally
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Research Indicates the creation of 1.4 L to 1.9 L more Deep Learning talent positions over the next few years
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Between 2 Million – 4 million projected demand for business translators over the next decade
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$3 Trillion Wages will be affected as Machine Learning gains better capabilities in Natural Language Understanding
Program Highlights / Benefits to the Participants
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Uncover new Growth Opportunities for Business and be at the forefront of Technology Innovation
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Access and Analyse structured and unstructured data at a level that has been until now, unimaginable. Understand Business Data better and be able to generate trend and get Insights
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Help Businesses achieve new levels of intelligence and efficiency by designing intelligent business processes
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Gauge Customer Internal and External Needs and Satisfaction
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Diagnose and resolve Business Issues at faster pace
Program Structure & Details
Why Enrol for this Program?
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The program covers extensively used ML & AI applications and technologies including Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Artificial Neural Network and TensorFlow.
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The sessions are conducted in classroom by the industrial professional on weekends to get a learning environment that causes minimal disruptions to your work schedule.
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Throughout the program, the candidates would be exposed to various hands-on mini projects based on real-life problem statements under the guidance of mentor.
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The candidates will also be trained to use GitHub and showcase all their projects on it, which can act as their portfolio.
Course: Foundation
Duration: 4 weeks
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Program Orientation
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Revisiting Mathematics and Statistics
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Introduction to R Programming
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Introduction to Python Programming
Course: Foundation
Duration: 4 weeks
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Program Orientation
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Revisiting Mathematics and Statistics
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Introduction to R Programming
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Introduction to Python Programming
Course: Machine Learning
Duration: 6 weeks
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Machine Learning – Supervised Learning : Regression, Classification Algorithms
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Machine Learning – Unsupervised Learning : Clustering, Dimensionality Reduction, Association Rules Mining
Course: Advanced Machine Learning
Duration: 2 weeks
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Model Evaluation and Selection
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Ensemble Methods
Course: Artificial Intelligence
Duration: 4 weeks
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Deep Learning
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Computer Vision
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Natural Language Processing
Live Project
Duration: 4 weeks
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Image Recognition using Deep Learning
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Face Detection using Deep Learning
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Different Projects on Natural Language Processing
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Customer Segmentation using Clustering
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RFM Analysis
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Market Basket Analysis using Association Rules
Learning Approach
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The Program features 16 weekends of classroom learning and lab work designed to instil rigour, knowledge and real-world understanding of ML & AI
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This is followed by a 4-week exposure on Industry Live Projects that helps translate the gained knowledge in the first part of the program into actual real-world execution
Key Program Deliverables
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StrataHive will award “Certificate of Completion” to participants at the end of the program
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160 hours of face-to-face Classroom Sessions
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Study Material – Presentations, References
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Activities and Practices on Datasets
