Artificial Intelligence(AI)

Linear algebra (vectors, matrices, operations)

Calculus (differentiation, integration)

Probability theory and statistics (probability, random variables, distributions, hypothesis testing)

Supervised learning, unsupervised learning, and reinforcement learning Training, validation, and test sets

Performance metrics (accuracy, precision, recall, F1 score)

Overfitting and underfitting

Feature selection and feature engineering

Linear regression

Logistic regression

Decision trees and random forests

Naive Bayes

Support vector machines (SVM)

K-nearest neighbors (KNN)

Clustering algorithms (k-means, hierarchical clustering)

Dimensionality reduction techniques (PCA, t-SNE)

Neural networks and their architecture

Activation functions (sigmoid, ReLU, etc.)

Backpropagation algorithm

Convolutional neural networks (CNN) for image recognition

Recurrent neural networks (RNN) for sequence data

Long short-term memory (LSTM) networks

Generative adversarial networks (GANs)

Transfer learning and fine-tuning

Text preprocessing (tokenization, stemming, lemmatization)

Bag-of-words and TF-IDF representations

Word embeddings (Word2Vec, GloVe)

Recurrent neural networks (RNN) for text processing

Attention mechanisms

Language modeling

Named Entity Recognition (NER)

Sentiment analysis and text classification

Markov Decision Processes (MDPs)

Value iteration and policy iteration

Q-learning and Deep Q-Networks (DQN)

Policy gradients and actor-critic methods

Exploration vs. exploitation trade-off

Computer vision (object detection, image segmentation)

Speech recognition and synthesis

Autonomous systems (self-driving cars, drones)

Recommendation systems

Natural language understanding and chatbots

AI in healthcare, finance, and other industries

Bias and fairness in AI systems

Privacy concerns and data protection

Transparency and interpretability

Algorithmic accountability

AI and job displacement

Implementing machine learning and deep learning algorithms

Building and training neural networks using popular frameworks (TensorFlow, PyTorch)

Solving real-world problems using AI techniques

Analyzing and interpreting AI models' results

Make a plan about how we can achieve our goal with deadline

Discussed & finalize Project definition

Define difficulties and solutions for project definitions

Research Analytics on project definition

Prepare Documents as : Wireframing, Document of Requirement, Target Audience

Any graduate Can make their career into Front-End development, Web Designing or web developers