6-Month Generative AI Learning Plan

• Basic syntaxRules for writing code, data typese.g., integers, strings, booleans, operatorsSymbols like +, -, *, control flowif/else statements, loops
• Data structuresWays to organize data: lists, tuples, dictionaries, sets
• FunctionsReusable blocks of code, modulesFiles containing Python code, OOP basicsObject-Oriented Programming concepts
• Error handlingUsing try/except blocks, file handlingReading and writing files

• "Python for Everybody" by Charles Severance (Coursera)
• "Python Crash Course" book
• "Learn Python 3 the Hard Way" book
• "Codecademy" Python 3 course

HackerRank, LeetCode (start with easy problems).

• NumPy arraysMulti-dimensional data structures, operationsMathematical operations on arrays, indexingAccessing specific elements
• Pandas DataFramesTabular data structure and SeriesOne-dimensional labeled array, data manipulationCleaning, transforming, merging data
• Matplotlib and Seaborn for basic data visualizationCreating charts and graphs

• "Data Analysis with Python" by IBM (Coursera)
• "Python for Data Science and Machine Learning Bootcamp" by Jose Portilla (Udemy)

NumPy, Pandas, Matplotlib, Seaborn.

Work through exercises and small projects.

• Linear AlgebraVectors, matrices, matrix operations, eigenvalues, eigenvectors
• CalculusDerivatives, integrals, gradient
• Probability and StatisticsBasic probability, distributions, descriptive statistics

• "Mathematics for Machine Learning Specialization" by Imperial College London (Coursera)
• Khan Academy (Linear Algebra, Calculus, Statistics & Probability sections).

"Deep Learning" book by Goodfellow, Bengio, and Courville (Mathematical notation chapters).

• Supervised LearningLearning from labeled data: Linear RegressionPredicting continuous values, Logistic RegressionPredicting categories, Decision TreesTree-like model for decisions, Random ForestsEnsemble of decision trees, Support Vector Machines (SVMs)Finding optimal separating hyperplane
• Unsupervised LearningLearning from unlabeled data: Clustering (K-Means)Grouping similar data points, Dimensionality Reduction (PCA)Reducing the number of features
• Model EvaluationAssessing model performance: Metrics, Cross-validation, Bias-Variance Tradeoff
• Basic Feature EngineeringCreating new features from existing ones techniques

• "Machine Learning" by Andrew Ng (Coursera)

"Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron.

scikit-learn.

• Introduction to Neural NetworksInterconnected nodes for computation: PerceptronsBasic building block of neural networks, activation functionsIntroduce non-linearity
• Multi-Layer Perceptrons (MLPs)Neural networks with multiple layers
• Backpropagation algorithmMethod to train neural networks
• Convolutional Neural Networks (CNNs)For image processing: Architecture, applications
• Recurrent Neural Networks (RNNs)For sequence data: Architecture, applications
• Introduction to Deep Learning Frameworks: TensorFlowOpen-source DL library by Google and/or PyTorchOpen-source DL library favored by researchers (choose one)
• Training Deep Neural Networks: Optimization algorithms (Adam, SGD)Methods to update network weights, regularization techniques (dropout, batch normalization)Preventing overfitting, handling overfittingModel performs well on training data but not on unseen data

• "Deep Learning Specialization" by deeplearning.ai (Coursera)
• "TensorFlow in Practice Specialization" (Coursera) OR "PyTorch for Deep Learning" (Coursera)
• "FastAI" courses

"Deep Learning" book.

TensorFlow or PyTorch.

• Fundamentals of Generative ModelingCreating new data similar to training data
• Variational Autoencoders (VAEs)Probabilistic generative model: Architecture, training process, applications
• Generative Adversarial Networks (GANs)Framework with a generator and discriminator: Architecture, training dynamics, common GAN architectures (DCGANDeep Convolutional GAN)
• Introduction to Diffusion ModelsGenerative models based on denoising: Basic concepts and intuition
• Applications of Generative Models: Image generation, text generation, music generation, etc.

• "Generative Adversarial Networks (GANs) Specialization" (Coursera)
• Search for specific courses on VAEs and Diffusion Models on Coursera, Udemy, Udacity.

"Generative Adversarial Nets", Foundational papers on VAEs.

Search for practical implementations of basic GANs and VAEs using TensorFlow or PyTorch.

• Implement DCGAN on CIFAR-10
• Implement VAE on MNIST
• Experiment with text generation using pre-trained models from Hugging Face Transformers
• Implement a basic diffusion model

Hugging Face Transformers, Keras Examples, PyTorch Tutorials.

Explore open-source implementations (understand the code, don't just copy).

Create a website (e.g., using GitHub Pages, Netlify) to showcase your projects and skills. Host code on GitHub with clear READMEs.

Tailor to highlight your AI/ML and Generative AI skills and projects.

Focus on Python, ML fundamentals, deep learning, and basic generative model concepts. Use platforms like LeetCode for coding practice.

Indeed, LinkedIn, Glassdoor, AngelList.

Explore advanced GAN architectures, Stable Diffusion, Transformer models in more detail based on your interests.