The AI Engineer’s Path to Success: Code, Train, Deploy
Everything you need to know about AI Engineering - Hands-on from Algorithms, Programming to Real Projects
Updated on Jan, 2025
Language - English
Development ,Software Engineering,Data Structures
Lectures -164
Duration -40 hours
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Course Description
Ooh, great. Well, the AI Mastery bootcamp is an integrated and entirely field-based program designed to equip fresh novices with all the necessary knowledge and skills to graduate to professional AI engineers. Gradually over the period of sixteen weeks, you will learn all the steps in building, training and deploying machine learning models using some of the most advanced tools and techniques all around. This boot camp is very hands on as much as it does build capacity within you to leverage artificial intelligence when it comes to solving real live challenges and also creating new, novel solutions.
It's all conservative and progressive, with the initial topics including the basics of Python programming, things like data preprocessing and into introductory machine learning. You will then start gradually, advancing to things like neural networks, deep learning and natural language processing. A grasp of all essential AI frameworks like TensorFlow, PyTorch and that of Hugging Face, which are fantastic for modern day AI development, will also be part of the program.
Now, the boot camp is ideal for anyone genuinely interested in artificial intelligence, starting with nothing or just wanting to take skills to the next level. Exposure to AI is not a prerequisite. All one needs is the willingness to learn and explore. The training will provide the tools and know how to build formed AI solutions from scratch, thus readying candidates for industry challenges or opportunities for more advanced research in AI.
Exciting travel with us is a portion of the future technology.
Goals
Technical Skills
- Understand the basic fundamentals of programming in Python for AI and data science applications.
- Understand fundamental machine learning algorithms and methodology.
- Learn about deep learning, neural networks, and TensorFlow.
- Know about natural language processing and computer vision.
- Understand and apply Generative AI, including large language models.
Practical Application
- Implement project-based solutions to real-world AI solutions.
- Prepare a proper capstone project focusing on the skills of AI and ML.
- Familiarity with the tools and frameworks followed in the industry.
Professional Development
- Prepare for the certifications like Microsoft Azure AI Engineer.
- Skills that fit into multiple AI and ML job roles.
- Using AI in everyday tasks and decision-making.
Ethical Issues
- Understanding AI Ethics including legal issues and data privacy.
- Algorithmic biases and the problem of data reproducibility.
Thus, keeping these aspects under one roof, this AI Bootcamp would be an all-inclusive learning experience combining theoretical and practical aspects of learning, making preparing students for successful careers in the evolving field of artificial intelligence.
Prerequisites
- Foundational Math Skills: Understanding of algebra and basic calculus concepts (derivatives, functions) for ML
- Interest in AI and ML: A passion for learning AI, machine learning and data driven technologies
- Laptop/Computer: A device capable of running data processing and ML libraries like TensorFlow, PyTorch and Docker
- Curiosity and Perseverance: Willingness to solve problems, experiment with data and work through challenges
Curriculum
Check out the detailed breakdown of what’s inside the course
Week 1: Python Programming Basics
9 Lectures
-
Introduction to Week 1 Python Programming Basics
00:38
00:38
-
Introduction to the Entire Bootcamp
01:46
01:46
-
Day 1: Introduction to Python and Development Setup
20:37
20:37
-
Day 2: Control Flow in Python
32:46
32:46
-
Day 3: Functions and Modules
23:22
23:22
-
Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)
30:33
30:33
-
Day 5: Working with Strings
23:53
23:53
-
Day 6: File Handling
22:48
22:48
-
Day 7: Pythonic Code and Project Work
39:28
39:28
Week 2: Data Science Essentials
8 Lectures
Week 3: Mathematics for Machine Learning
8 Lectures
Week 4: Probability and Statistics for Machine Learning
8 Lectures
Week 5: Introduction to Machine Learning
8 Lectures
Week 6: Feature Engineering and Model Evaluation
8 Lectures
Week 7: Advanced Machine Learning Algorithms
8 Lectures
Week 8: Model Tuning and Optimization
8 Lectures
Week 9: Neural Networks and Deep Learning Fundamentals
8 Lectures
Week 10: Convolutional Neural Networks (CNNs)
8 Lectures
Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling
8 Lectures
Week 12: Transformers and Attention Mechanisms
8 Lectures
Week 13: Transfer Learning and Fine-Tuning
8 Lectures
Machine Learning Algorithms and Implementations in Python
28 Lectures
LangChain for Beginners
6 Lectures
Miscellaneous Projects on AI for Daily Practice
25 Lectures
Instructor Details
Vivian Aranha
After completing my Master's in Computer Science at George Washington University, started working on Web and Mobile Applications. Have been training people in Mobile Technologies since 2009 in both iOS and Android Technology.
Been building Mobile Apps and Websites since 2002 for brands like Delta Air Lines, The Washington Post, Cars, Kaplan, and many more. Have more than 150 mobile applications of my own.
Hands-on experience architecting, developing, and publishing apps since 2009.
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