Certified Data science with AI

The Certified Data Scientist with AI program goes beyond traditional data science by immersing you in the advanced world of AI. Artificial Intelligence is the driving force behind many modern technological advancements, from smart assistants and self-driving cars to recommendation systems and predictive analytics.

Through this program, you will learn how AI algorithms can enable data processing on an enormous scale, recognizing patterns for decision-making and also improving overtime without the need for human beings.

You will also study practical applications of AI in healthcare, finance, retail and other fields where it finds usage among others. By mastering this discipline, you will be ready to create intelligent systems that wouldn’t just analyze data but would also help index future trends, automate processes or even create solutions to complex issues faced today.

Course Content:

• Module 1: Introduction to Data Science and AI
• It’s time to start your first steps in the world of data science and AI, attempting to understand how data science is enhanced by AI through, for example, machine learning, deep learning, or neural networks. Check smart assistants, predictive analytics and other areas of application.
• Module 2: Data Collection and Preprocessing
• In this module, it is going to be explained how to look for information and extract it from different accessible sources and further prepare this information for future research. This module addresses data cleansing, absence of information handling, data normalization techniques, and data features engineering. You will also gain some diagnostics that will be based on the use of the visual representation of data.
• Module 3: Statistics and Probability for AI
• This module explains the basic statistical parameters, such as the mean, variance, and standard deviation, as well as some probability distributions and even the basics of statistical hypothesis testing. Get to know some effective data analysis techniques using Bayesian inference and how it helps AI systems conduct more effective model training.
• Module 4: Machine Learning Fundamentals
• You will learn about basic machine learning-related concepts like supervised learning, unsupervised learning, and reinforcement learning. The most commonly used algorithms like linear regression, decision trees, and clustering will be highlighted. Understand how some indicators such as accuracy and f1-score are applied in model performance evaluation and model approximation process
• Module 5: Deep Learning and Neural Networks:
• This module allows students to study deep learning from a neural network aspect: Architecture, layer, activation function. Students will study CNN for image processing and RNN for sequential data using TensorFlow and PyTorch.
• Module 6: Advanced AI Techniques
• Study advanced AI techniques like NLP-text analysis, sentiment detection, and chatbot creation. Computer vision for image classification and generative models including GANs are other important aspects. A short Introduction to reinforcement learning is also given.
• Module 7: AI in Action: Industry Applications
• View demonstrations of AI in action across industries: health care, finance, retail, and many more. Learn about AI-powered predictive diagnostics, fraud detection and recommendation systems. Learn about the use of AI in supply chain optimization and predictive maintenance.
• Module 8: AI Model Deployment and Automation
• Master various model deployment strategies, from Cloud to on-premises. MLOps for continuous integration and deployment of machine learning models using tools like Docker and Kubernetes for serving models efficiently.
• Module 9 Ethics, Privacy, and Future of AI :
• Discussions on bias, equity, and transparency in AI on ethical ground, data privacy, and security. Also, discussions will be on future AI trends, challenges, and career opportunities in this emerging field.
• Module 10: Capstone Project
• Wrap up with a capstone in which you’ll apply your skills to solve a real-world problem; this project will be an end-to-end hands-on activity in which you will demonstrate your skills in creating and deploying AI models to solve industry-specific problems, from problem definition to deploying the solution.

Learning Outcomes:

• Gain deep knowledge about AI and its application in data science.
• Independently perform data preprocessing, feature engineering, and exploratory data analysis.
• Gain understanding of machine learning algorithms
• Learn how to deploy AI models with modern tools and best practices.
• Put your AI skills into practice with a real-world capstone project across a diversity of industries.