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Software Engineering Fundamentals | Machine Learning in Production
Data Wrangling at Scale and Statistics for AI | Machine Learning Case Studies
Machine Learning Capstone
Description Given Below 410 students enrolled
Machine learning is the science of getting computers to act without being explicitly programmed. We all know that in recent times, machine learning has given us self-driving cars, practical speech recognition, effective web search. Machine learning is so relevant today that you probably use it dozens of times a day without even knowing the same.
In this course, you will learn about the most effective machine learning techniques, and gain practical knowledge implementing them. You will learn the practical know-how needed to quickly and powerfully apply these techniques to new issues. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.
Topics included are Supervised learning, Unsupervised learning & the best practices in machine learning You will be working and learning from a numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots, text understanding , computer vision, medical informatics, audio, database mining, etc. The rules for perception & control & the methods of web search & anti-spam will be done.
Students will build a solid foundation in Supervised, Unsupervised, Reinforcement, and Deep Learning. and also Learn advanced machine learning techniques like Statistics, Regression and Clustering Algorithms, Neural Networks, RNNs, CNNs, Lexical, Syntactical and Semantic Processing as well as including deployment to a production environment Our Machine learning Training with guaranteed placement program helps students secure their job after the Machine learning Training.
All Graduates from BCA, BE, BSCIT, MCA, BTECH who are interested in Machine learning & AI Domain are eligible for this Machine learning Training.
We introduce the principles and the critical analysis of the main paradigms for learning from data and their applications.
The course provides the Machine Learning basis for both the aims of building new adaptive Intelligent Systems and
powerful predictive models for intelligent data analysis.
Our syllabus includes training on the latest advancements and technical approaches in Artificial Intelligence
& Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning. THe Modules covered basically are
• Computational learning tasks for predictions, learning as function approximation, generalization concept.
• Linear models and Nearest-Neighbors (learning algorithms and properties, regularization).
• Neural Networks (MLP and deep models, SOM).
• Probabilistic graphical models.
• Principles of learning processes: elements of statistical learning theory, model validation.
• Support Vector Machines and kernel-based models.
• Introduction to applications and advanced models.
• Python Programming Certification Course
• Machine Learning Certification Training using Python
• Graphical Models Certification Training
• Reinforcement Learning
• Natural Language Processing with Python Certification Course
• AI & Deep Learning with TensorFlow
• Python Spark Certification Training using PySpark
For Data Science Modules the following Modules will be covered in deep,
• Data Science R Programming
• Data Science SAS Training
• Data Science Python
• Machine Learning*
• Tableau Training
• Big Data Hadoop and Spark Developer
• Data Science Capstone
The entire Modules & syllabus brochure shall be given during class lectures.