What You’ll Learn Fundamental Of AI
- What is Artificial Intelligence?
- History & Evolution of AI
- AI vs Machine Learning vs Deep Learning
- Applications of AI in different domains
- Ethical considerations in AI
Basic Mathematics for AI
- Linear Algebra (Vectors, Matrices, Eigenvalues, Eigenvectors)
- Probability & Statistics (Bayes Theorem, Probability Distributions)
- Calculus (Derivatives, Integrals, Chain Rule for Optimization)
- Python Basics (Variables, Data Types, Loops, Functions)
- Libraries: NumPy, Pandas, Matplotlib, Seaborn
- Object-Oriented Programming (OOP) concepts
- Git & Version Control
- ML Workflow (Data Preprocessing, Feature Engineering, Model Selection)
- Overfitting vs Underfitting Machine Learning Basics
- Evaluation Metrics (Accuracy, Precision,Recall, F1 Score)
Supervised vs Unsupervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees & Random Forest
- Support Vector Machines (SVM)
- Neural Networks Basics Unsupervised Learning Algorithms
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Dimensionality Reduction
- Cross-Validation Techniques
- Hyperparameter Tuning (Grid Search, Random Search)
- Feature Selection & Engineering Machine Learning Basics Introduction to Deep Learning
- Basics of Neural Networks
- Activation Functions (ReLU, Sigmoid, Tanh)
- Loss Functions (MSE, Cross-Entropy)
- Backpropagation & Gradient Descent
- Fully Connected Networks (Feedforward Neural Networks)
- Training Deep Learning Models using TensorFlow & PyTorch
- Optimizers (SGD, Adam, RMSProp) Convolutional Neural Networks (CNNs)
- Image Processing Basics
- CNN Architecture (Convolutions, Pooling, Fully Connected Layers)
- Transfer Learning with Pre-trained Models (ResNet, VGG, EfficientNet)
- Sequential Data & Time Series Processing
- LSTMs & GRUs
- Introduction to Transformers (Self-Attention, Multi-Head Attention)
- BERT & GPT Architectures Generative AI Fundamentals
- What is Generative AI?
- Generative Adversarial Networks (GANs) Overview
- Variational Autoencoders (VAEs)
- Text Generation with LSTMs & Transformers
- Image Generation with GANs (DCGAN, CycleGAN, StyleGAN)
- Audio & Video Generation Models
- Large Language Models (LLMs)
- Understanding LLMs (BERT, GPT-3, GPT-4, LLaMA)
- Tokenization & Embeddings
- Prompt Engineering Techniques
- Fine-Tuning LLMs for Specific Use Cases
- APIs for AI (OpenAI API, Hugging Face)
- Model Deployment using FastAPI & Flask
- Using LangChain & RAG (Retrieval-Augmented Generation)
- Cloud Deployment (AWS, GCP, Azure)
- AI in Healthcare (Disease Prediction, Medical Image Analysis)
- AI in Finance (Fraud Detection, Risk Analysis)
- AI in Robotics & Automation
- AI for Content Creation (Text, Image, Video)
- Beginner: Predictive Analytics on Tabular Data
- Intermediate: Chatbot using LLMs Hands-on Projects
- Image Generation with GANs (DCGAN, CycleGAN, StyleGAN)
- Audio & Video Generation Models
- Advanced: Generating Images & Videos using Generative AI
- AI Model Lifecycle Management (MLOps)
- Integrating AI into Web & Mobile Apps
- Ethical AI & Bias Mitigation
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