Data to Intelligence: AI & amp; ML in Practice
About This Course
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing industries worldwide, from healthcare and finance to retail and cybersecurity. As businesses increasingly rely on data-driven decision-making, the demand for AI and ML professionals has skyrocketed. The Data to Intelligence: AI & ML in Practice certification program is designed to equip learners with the necessary skills to transition from handling raw data to building intelligent AI-driven solutions. This 3-month intensive program provides a structured, hands-on approach to AI and ML, ensuring a solid foundation and practical expertise in real-world applications.
Course Objectives
By the end of this certification program, participants will be able to:
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Understand AI & ML Fundamentals: Gain knowledge of core AI/ML concepts, techniques, and real-world applications.
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Process and Analyze Data: Learn data collection, cleaning, transformation, and feature engineering to improve model performance.
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Develop Machine Learning Models: Implement supervised and unsupervised learning techniques for predictive analytics and decision-making.
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Master Deep Learning & Neural Networks: Build and train neural networks for applications like image recognition and NLP.
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Optimize and Tune AI Models: Improve accuracy and efficiency using hyperparameter tuning and optimization strategies.
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Deploy AI Models in Production: Learn model deployment techniques using Flask, FastAPI, and cloud platforms like AWS and Google Cloud.
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Apply AI in Various Industries: Understand how AI is used in healthcare, finance, retail, and cybersecurity.
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Ensure Ethical AI Practices: Learn about fairness, bias mitigation, and responsible AI development.
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Complete a Capstone Project: Develop a real-world AI/ML solution from scratch, demonstrating end-to-end expertise.
Learning Objectives
1. Understand fundamental AI and ML frameworks, methodologies, and problem-solving strategies.
2. Develop expertise in supervised and unsupervised learning, feature engineering, and model selection for practical applications.
3. Gain proficiency in advanced machine learning techniques, including logistic regression, SVM, dimensionality reduction, neural networks, and decision trees.
4. Apply reinforcement learning, PCA, and graphical models to real-world AI problems, improvingpredictive accuracy and decision-making
5. Learn optimization strategies and parameter estimation for high-performance models.
6. Explore unsupervised learning, clustering techniques, and ensemble methods.
Material Includes
- E Learning materials
Target Audience
- Data Scientists amp; ML Engineers – Building and deploying AI/ML models.
- Software Developers – Integrating AI into applications.
- Business Analysts & Decision Makers – Leveraging AI for strategic insights.
- Researchers & Academics – Exploring AI methodologies and applications.
- Students & AI Enthusiasts – Gaining hands-on experience in AI/ML.
- Product Managers & Tech Executives – Understanding AI-driven innovation.
- Industry Professionals – Applying AI across sectors like finance, healthcare, and tech.
Curriculum
31 Lessons2160h
MODULE 1
Introduction16:19
Types of Machine Learning00:9:25
Discriminative Models, Gradient Descent Algorithm00:8:49
Prediction Modelling and Applications00:7:22
Probabilistic Interpretation and Regularization00:6:56
MODULE 1 – QUIZ
MODULE 2
MODULE 3
ADDITIONAL PPT’S
Your Instructors
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₹19,999.00
Material Includes
- E Learning materials
Data Science
₹1,999.00
