AI for developers fundamentals (Digital Stack / Google, 2023-2024)
Info
- Project type Online course
- Academy Digital Stack & Google
- Period 20th of Nov 2023 - 17th of Jan 2024
- Duration 30 hours
- My Role Mentor/Trainer
- Topics AI, ML, DL, NN, LLM
- Keywords AI, Neural Networks, Deep Learning, Machine Learning, Knowledge, Similarity, Semantic, Natural Language Processing, Reasoning, Python, Java, OOP, Clustering, Classification, Decision Tree, SVM
- Skills developed Public speaking, Tech presentation
- Curricula PDF
Description
Starting in November 2023, after speaking at the DevFest conference in Bucharest on the topic of Neural Networks, I embraced the opportunity to mentor a class of AI students for nearly three months at Atelierul Digital - an educational program developed by Digital Stack and Google.
The students in the class had diverse backgrounds, coming from various technical faculties across Romania. Their fields of study included:
- Electronics and Telecommunications
- Information Technology
- Automatics and Computer Sciences
- Cybernetics
- Mathematics-Informatics
My responsibilities included developing the curriculum, preparing educational materials (slides, documentation, references), managing communication with students, leading technical sessions, and devising the final evaluations.
During the course, I enlisted the help of my colleague Diana, an experienced Machine Learning engineer with a PhD. She has prior teaching experience at the Faculty of ETTI and assisted me in the Machine Learning section.
Our sessions encompassed both theoretical and practical elements.
In the theory section, we discussed mathematical and technical concepts essential for understanding the fundamentals of AI, ML, and Data Science.
In the practical section, we conducted hands-on labs using Python, Java, and JavaScript. These labs focused on building AI models and Neural Networks from scratch, training and testing these models, and graphically visualizing and analyzing the results.
Here are the detailed curricula:
- Intro to AI
- What is AI
- Intelligent Agents
- Advantages & Disadvantages of AI
- Challenges of AI
- Problem Solving
- What is Searching
- Uninformed Search Algorithm
- Informed Search Algorithm
- Adversarial Search
- Constraint satisfaction problems
- Knowledge Representation & Planning
- Knowledge Representation Techniques
- Propositional, First order Logic Logic
- RuleBased System
- Probabilistic Reasoning
- Basic Probability Concepts
- Markov and Hidden Markov Model
- Association rules
- Dimensionality reduction
- Feature selection and Feature Extraction
- Python & OOP Basics
- Setup
- Data structures
- Control flow
- Functions, Modules
- Conda
- NumPy, Tensorflow, Pytorch
- Pandas, Matplotlib
- Machine Learning
- What is Machine Learning?
- Types of Learning
- Unsupervised: Clustering & Dimensionality Reduction
- Reinforcement learning
- Semisupervised
- Regression
- Classification
- Overfitting, Underfitting and Model Selection
- Practical Example
- Communication and Perceiving
- Natural Language Processing
- Perception
- Computer vision
- Deep Learning & Neural Networks
- Neuron
- Neural Networks fundamentals
- Components & Architectures
- Activation, Cost, Brakpropagation, Gradient Descent
- Optimization
- NN Frameworks
- NN advanced concepts
- Data Mining
- Data Modeling & Mining
- Data Mining Tools
- AI Ethics
- Understanding AI Ethics
- Pitfalls
- Strategies
- AI Tools & trends
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- Curricula
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- Course description
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- Program Mentors
