Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
5 Hour(s) 58 Minute(s)
Language
English
Taught by
School of AI
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
AI Algorithms, AI Models, AI Agents, Python to 1000 Real-World AI Projects, AI Agents, MCP, Google A2A, more(AI)
With tools like ChatGPT, DeepSeek AI, Mistral, Claude (Anthropic AI), Perplexity AI, Google Gemini, Microsoft Copilot, Jasper AI, Meta AI, Chatsonic, GitHub Copilot, YouChat, and Writesonic on the rise everyone needs to put Artificial Intelligence on their Radar.
Welcome to the Artificial Intelligence Mastery: Complete AI Bootcamp 2025! This comprehensive Artificial Intelligence Bootcamp is your ultimate guide to becoming a skilled AI Engineer, empowering you to master Artificial Intelligence and apply it to real-world problems. Over an intensive 16-week Artificial Intelligence training program, you'll gain hands-on experience in building, training, and deploying AI models using the latest AI tools and frameworks.
In this Artificial Intelligence Bootcamp, you'll start with the fundamentals of Artificial Intelligence, including Python programming, data preprocessing, and an introduction to machine learning. As you progress, you'll explore advanced Artificial Intelligence concepts such as neural networks, deep learning, natural language processing (NLP), and computer vision. You'll also master industry-standard AI frameworks like TensorFlow, PyTorch, and Hugging Face, essential for modern AI development and deployment. This is the #1 Course on Udemy for AI.
This AI Bootcamp 2025 focuses heavily on practical AI skills, ensuring that every module comes with real-world projects to strengthen your understanding. Whether you're an AI beginner or someone looking to expand their AI expertise, this course is designed for you.
By the end of the AI Mastery Bootcamp, you'll have the AI skills, confidence, and hands-on experience to build and deploy AI solutions from scratch. You’ll be fully prepared to tackle industry AI challenges, contribute to AI research, or innovate in your own AI-driven projects.
Key Highlights of the AI Mastery Bootcamp:
Comprehensive AI Curriculum covering Python, Machine Learning, Deep Learning, NLP, and AI Frameworks
Hands-On AI Projects to build practical AI skills
Real-World AI Applications and case studies
Industry-Standard AI Tools: TensorFlow, PyTorch, Hugging Face
Beginner-Friendly AI Program with step-by-step guidance
Whether you're aiming to become an AI Engineer, AI Researcher, or a leader in the AI industry, this Artificial Intelligence Bootcamp will equip you with the tools, knowledge, and experience you need.
Join the AI Revolution Today – Enroll in the Artificial Intelligence Mastery: Complete AI Bootcamp 2025 and become a leader in the world of AI!
Course Content
- 43 section(s)
- 799 lecture(s)
- Section 1 Introduction to AI Mastery Course
- Section 2 Week 1: Python Programming Basics for Artificial Intelligence
- Section 3 Week 2: Data Science Essentials for Artificial Intelligence
- Section 4 Week 3: Mathematics for Machine Learning and Artificial Intelligence
- Section 5 Week 4: Probability and Statistics for Machine Learning and Artificial Intellige
- Section 6 Week 5: Introduction to Machine Learning
- Section 7 Week 6: Feature Engineering and Model Evaluation
- Section 8 Week 7: Advanced Machine Learning Algorithms
- Section 9 Week 8: Model Tuning and Optimization
- Section 10 Week 9: Neural Networks and Deep Learning Fundamentals
- Section 11 Week 10: Convolutional Neural Networks (CNNs)
- Section 12 Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling
- Section 13 Week 12: Transformers and Attention Mechanisms
- Section 14 Week 13: Transfer Learning and Fine-Tuning
- Section 15 Machine Learning Algorithms and Implementations
- Section 16 Introduction to Machine Learning and TensorFlow
- Section 17 Basics of TensorFlow
- Section 18 Intermediate TensorFlow
- Section 19 Advanced TensorFlow
- Section 20 Practical Applications and Projects
- Section 21 Further Learning and Resources in TensorFlow
- Section 22 Introduction to Learning PyTorch from Basics to Advanced
- Section 23 LangChain for Beginners
- Section 24 AI Agents: A Comprehensive Overview
- Section 25 Creating and Publishing GPTs to ChatGPT Store
- Section 26 DeepSeek Projects
- Section 27 Deep Dive into Qwen 2.5
- Section 28 Introduction and Hands-on MLOps
- Section 29 Miscellaneous Projects on AI for Daily Practice
- Section 30 Machine Learning - Basic Projects
- Section 31 Machine Learning - Intermediate Projects
- Section 32 Deep Learning - Basic Projects
- Section 33 Deep Learning - Intermediate Projects
- Section 34 Natural Language Processing Projects
- Section 35 Computer Vision Projects
- Section 36 Reinforcement Learning Projects
- Section 37 Time Series Analysis Projects
- Section 38 Recommendation Systems Projects
- Section 39 Generative Models Projects
- Section 40 Graph Neural Networks Projects
- Section 41 Healthcare AI Projects
- Section 42 More Projects
- Section 43 AI Systems Engineering: Projects in RAG, MCP, Agent2Agent and Autonomous Agents
What You’ll Learn
- Master Python for Artificial Intelligence: Write efficient Python code, essential for AI and ML programming tasks.
- Data Preprocessing Skills for Artificial Intelligence: Prepare, clean, and transform data to enhance model performance.
- Statistical Knowledge for Artificial Intelligence: Apply core statistics to understand data patterns and inform decisions.
- Build Machine Learning Models for Artificial Intelligence: Develop and fine-tune ML models for classification, regression, and clustering.
- Deep Learning Proficiency: Design and train neural networks, including CNNs and RNNs, for image and sequence tasks.
- Utilize Transfer Learning: Adapt pre-trained models to new tasks, saving time and resources.
- Deploy ML Models with APIs: Create scalable APIs to serve ML models in real-world applications.
- Containerize with Docker: Package models for portable deployment across environments.
- Monitor and Maintain Models: Track model performance, detect drift, and implement retraining pipelines.
- Complete ML Lifecycle: Master end-to-end AI project skills, from data to deployment and ongoing maintenance.
Skills covered in this course
Reviews
-
MMoran
Excellent course very coprehensive
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AAshu Singh
he doesnt even explain most of the code that he writes
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RRenee Dickens
I appreciated the cooking references/ explanations of AI definitions having a culinary background myself.
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KKiran Anand
Not finished yet, but there is a definite lack of explanation of syntax.
