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Course
Overview
The course covers machine learning fundamentals, practical implementation skills, domain-specific applications, data preprocessing, feature engineering, model evaluation, and ethics. It covers various industries and offers career advancement opportunities in machine learning engineering and business analytics.
Participants develop problem-solving skills through real-world projects, preparing them to tackle complex challenges. The course fosters innovation, competitiveness, adaptability, lifelong learning, networking, and decision-making.
It is tailored to various audiences, including students, academics, professionals, entrepreneurs, policymakers, and non-profit organizations, preparing participants for diverse applications and societal advancements.
Unique Pedagogy
The online course gives a detailed understanding of the latest technologies and developments in the finance domain. The sessions by Industry Experts are via Live interactive online sessions. Get hands-on training through a Capstone Project.
Industry Collaboration
The Professional Certification in Artificial Intelligence and Machine Learning course will enable you to take a design-centric approach and effectively utilise the latest tools and techniques to give you a competitive advantage in the age of changing information architecture, cybersecurity laws, Artificial Intelligence, Virtual reality, and Metaverse.
World Class Faculty
Industry experts from large conglomerates and academic experts with over 15 years of work experience in top management positions will help you develop your skills and impart real-time knowledge through this course.
Eligibility
These programs are designed to cater to professionals looking to advance their careers in AI and ML by providing flexible learning options and practical, hands-on experience. This is open to anyone interested in advancing their knowledge in AI and ML. No formal prerequisites exist, but familiarity with programming and mathematical concepts is recommended.
Curriculum
- Introduction to Python, Data Structures, Functions and Modules, File Handling and Exception, Introduction to NumPy, Introduction to Pandas, Data Visualization, Python Platform
- Introduction to AI and Machine Learning, Basics of statistice and Linear Algebra.
- Dependent and Independent variables, correlations, Feature Engineering, Linear and Logistics Regression
- Overfitting and Underfitting, Model Evaluation Techniques.
- Clasification Algorithms, Neural Network.
- NLTK tools and textdata..
- Introduction to Convolutional Neural Networks(CNN), Transfer Learning and Object Detection, Deep Sequential Models.
- Generative Adversarial Networks(GAN's), Practical Applications and Case Studies.
Program Fees
₹ 80,000 /-
Scholarship Applicable for Eligible Students
No cost 6 or 9 Month EMI Option
Available
Pro Certificate in AI and ML
- Installment 1
- Installment 2
Course fee - Installment
- ₹ 40,000 /-
- Within 2 months - ₹ 40,000 /-
Course fee - Lumpsum
- Discount- ₹ 5,000 /-
- -
- Total- ₹ 75,000 /-
Assessment
Artificial Intelligence and Machine Learning - Market Opportunities
1. On Roles and Responsibilities
Data Scientist:
Data scientists leverage AI and ML algorithms to analyze complex data sets and derive actionable insights.
Machine Learning Engineer
Engineers develop ML models and algorithms, focusing on implementation and deployment in real-world applications.
AI Research Scientist
Research scientists work on advancing AI technologies, developing new algorithms, and pushing the boundaries of what AI can achieve.
AI Consultant
Consultants provide expertise to organizations looking to integrate AI solutions into their operations or products.
Robotics Engineer:
Engineers design and develop AI-powered robots for various industries, including manufacturing and healthcare.
AI Product Manager:
Product managers oversee the development and implementation of AI-powered products and services.
AI Entrepreneur:
Entrepreneurial opportunities exist for those interested in starting AI-focused startups or consulting firms.
Natural Language Processing (NLP) Engineer:
Develop algorithms and applications that enable machines to understand and process human language.
Computer Vision Engineer:
Develop systems that can interpret visual data.
2. On Sectors
Technology
Companies like Google, Facebook, Microsoft, and startups focusing on AI-driven products and services.
Finance
Utilizing AI for fraud detection, algorithmic trading, risk management, and customer service automation
Healthcare
AI applications in medical imaging, personalized medicine, drug discovery, and patient care optimization.
Automotive
Autonomous vehicles, predictive maintenance, and smart transportation systems.
Retail
AI-powered recommendation systems, demand forecasting, and personalized customer experiences.
Manufacturing
AI for process optimization, predictive maintenance, quality control, and supply chain management.
Telecommunications
AI-driven customer service bots, network optimization, and predictive maintenance.
Government
AI applications in public services, cybersecurity, law enforcement, and urban planning.
Education
AI-enhanced learning platforms, personalized tutoring systems, and educational analytics.
Energy
AI for energy grid optimization, predictive maintenance of infrastructure, and renewable energy management.
