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Core concepts of Artificial Intelligence and Machine Learning
Data preprocessing and visualization using Python
Supervised & unsupervised learning techniques
Building neural networks with TensorFlow & Keras
Natural Language Processing (NLP) and chatbot creation
Computer Vision for image classification and detection
Model evaluation, tuning, and deployment
Ethical use of AI in real-world applications
Basic understanding of mathematics and statistics
Familiarity with programming (preferably Python)
A laptop/PC with internet access
No prior AI experience required — we'll start from the basics
Students and graduates looking to enter AI or data science
Working professionals aiming to upskill or switch careers
Tech enthusiasts interested in AI development
Developers seeking to build real-world AI applications
Researchers and analysts exploring machine learning models
Gain in-demand AI skills through this hands-on certification course. Learn machine learning, deep learning, NLP, and computer vision using real-world projects and tools like TensorFlow and PyTorch. Ideal for students, professionals, and tech enthusiasts looking to build intelligent systems and advance their careers in AI.
Explore the comprehensive modules below, each designed to guide you through the core concepts and practical techniques in Artificial Intelligence. From fundamental AI principles and machine learning algorithms to deep learning and natural language processing, you’ll gain hands-on experience building intelligent systems that learn, reason, and solve complex problems. Develop the skills and confidence to create innovative AI solutions that transform industries and drive the future of technology.
What is AI? History and applications
Types of AI: Narrow, General, and Super AI
Overview of AI tools and technologies
Python basics and libraries (NumPy, Pandas)
Data preprocessing and cleaning
Data visualization with Matplotlib and Seaborn
Supervised vs. unsupervised learning
Regression, classification, clustering techniques
Model evaluation and performance metrics
Hands-on: Scikit-learn implementation
Introduction to neural networks
Activation functions, loss functions, optimizers
Building and training models with TensorFlow/Keras
Hands-on: Image classification using CNNs
Text preprocessing and vectorization
Sentiment analysis, text classification
Transformers and language models (BERT overview)
Hands-on: Chatbot development
Bias, fairness, and explainability in AI
AI in healthcare, finance, and automation
Legal, social, and ethical considerations
End-to-end AI project with real data
Model deployment basics
Mock tests and interview preparation
Portfolio development
Quality learning, simplified and budget-friendly – just for you!
From doubts to dreams realized, Eduvizo made it happen for me!
Ashish Kumar
Software engineer
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