Tools and Technologies Covered
- Python: The most widely used language for ML and AI, thanks to its simplicity and powerful libraries (TensorFlow, Scikit-learn, Pandas). It allows you to build and train machine learning models efficiently.
- SQL: Helps in managing, retrieving, and querying data from databases, a crucial skill for working with real-world datasets.
- Linux: A preferred operating system for running AI models on servers and cloud platforms. Knowing basic commands will help you manage environments and automate tasks.
- Data Collection: Web scraping, APIs, and working with datasets.
- Data Cleaning and Preprocessing: Handling missing data, encoding categorical data, data scaling.
- Exploratory Data Analysis (EDA): Visualization techniques, identifying patterns, data distributions.
- Data Wrangling with Pandas: Advanced data manipulation and aggregation.
- Statistics Essentials: Descriptive and inferential statistics, hypothesis testing, regression analysis.
- Probability Theory: Probability distributions, Bayes’ theorem, and random variables.
- Introduction to Machine Learning: Supervised vs. unsupervised learning, model evaluation metrics.
- Popular Algorithms: Linear regression, decision trees, clustering, SVM, k-nearest neighbors.
- Advanced Algorithms: Ensemble methods, Random Forest, Gradient Boosting.
- Neural Networks Fundamentals: Understanding perceptrons, activation functions, backpropagation.
- Convolutional Neural Networks (CNNs): Concepts, image classification, and applications.
- Recurrent Neural Networks (RNNs): Sequence modeling, applications in NLP.
- Generative AI Overview: Types of generative models, applications, and ethics.
- Autoencoders: Variational Autoencoders (VAEs), denoising autoencoders, applications.
- GANs (Generative Adversarial Networks): Basics, training process, and applications in image generation.
- Diffusion Models: Understanding diffusion processes, denoising diffusion probabilistic models.
- Text Preprocessing: Tokenization, stopword removal, stemming, and lemmatization.
- Traditional NLP Models: TF-IDF, word embeddings, and LSTM-based models.
- Transformers: Understanding transformer architecture, BERT, and GPT models.
- Text Generation with Generative AI: Using language models for text generation, summarization, chatbots.
- GAN Variants: Conditional GANs, CycleGANs, applications in style transfer, data augmentation.
- Diffusion and Other Emerging Models: Overview of cutting-edge generative AI models, use cases.
- Prompt Engineering for Generative Models: How to craft effective prompts, prompt-based fine-tuning.
- Fine-Tuning Pre-trained Models: Techniques for adapting pre-trained models to specific tasks.
- Image Generation with GANs and VAEs: Image synthesis, super-resolution, image-to-image translation.
- Generative AI in Audio: Text-to-speech, speech synthesis models, applications in music generation.
- Video Generation: Deepfake technology, ethical implications, tools for video generation.
- Ethics and Security in Generative AI: Addressing biases, privacy, and responsible AI practices.
- Healthcare Applications: Drug discovery, diagnostics, medical imaging.
- Content Creation: Automated content generation, creative AI tools for marketing.
- Gaming and Entertainment: Procedural content generation, AI-based game character design.
- Generative AI in Business: Chatbots, automated summarization, personalized marketing.