Course description
- Enhanced Efficiency and Productivity
- Data-Driven Decision Making
- Improved Customer Experience
- Innovation and Problem Solving
- Healthcare Advancements
- Enhanced Security
- Accessibility and Inclusivity
- Economic Growth
- Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.
- These processes include learning (acquiring information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
- AI has become an integral part of various industries, transforming how we live, work, and interact with technology.
- Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.
- These processes include learning (acquiring information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
- AI has become an integral part of various industries, transforming how we live, work, and interact with technology
- Overview of Artificial Intelligence
- History and Evolution of AI
- AI Applications in Various Industries
- Ethical Considerations in AI
- Setting Up the Development Environment (Python, Jupyter Notebooks, Anaconda)
- Introduction to Python Programming
- Data Structures and Algorithms in Python
- Essential Python Libraries for AI (NumPy, Pandas, Matplotlib, Seaborn)
- Object-Oriented Programming in Python
- Linear Algebra
- Vectors and Matrices
- Matrix Operations
- Eigenvalues and Eigenvectors
- Calculus
- Derivatives and Integrals
- Partial Derivatives
- Chain Rule and Gradient Descent
- Probability and Statistics
- Probability Theory
- Statistical Distributions
- Hypothesis Testing
- Bayesian Statistics
- Supervised Learning
- Regression Algorithms (Linear Regression, Ridge, Lasso)
- Classification Algorithms (Logistic Regression, Decision Trees, Random Forests, SVM, KNN)
- Evaluation Metrics (MSE, RMSE, R², Accuracy, Precision, Recall, F1 Score, ROC-AUC)
- Unsupervised Learning
- Clustering Algorithms (K-Means, Hierarchical Clustering, DBSCAN)
- Dimensionality Reduction (PCA, LDA, t-SNE)
- Model Selection and Evaluation
- Cross-Validation Techniques
- Hyperparameter Tuning (Grid Search, Random Search)
- Neural Networks Basics
- Introduction to Neural Networks
- Activation Functions
- Loss Functions and Optimizers
- Deep Learning Frameworks
- TensorFlow Basics
- Keras Basics
- Convolutional Neural Networks (CNNs)
- Understanding CNN Architecture
- Building and Training CNNs
- Applications of CNNs (Image Classification, Object Detection)
- Recurrent Neural Networks (RNNs)
- Understanding RNN Architecture
- LSTM and GRU Networks
- Applications of RNNs (Time Series Prediction, Natural Language Processing)
- Text Processing and Feature Extraction
- Tokenization, Lemmatization, and Stemming
- Bag-of-Words and TF-IDF
- Text Classification
- Sentiment Analysis
- Spam Detection
- Advanced NLP Techniques
- Word Embeddings (Word2Vec, GloVe)
- Transformers and BERT
- Sequence-to-Sequence Models
- Introduction to Reinforcement Learning
- Key Concepts: Agents, States, Actions, Rewards
- Q-Learning and Deep Q-Networks (DQNs)
- Applications of Reinforcement Learning (Game Playing, Robotics)
- Image Processing Basics
- Image Preprocessing and Augmentation
- OpenCV Basics
- Advanced Computer Vision Techniques
- Object Detection (YOLO, SSD)
- Image Segmentation (U-Net, Mask R-CNN)
- Generative Adversarial Networks (GANs)
- AI Ethics and Fairness
- Understanding Bias in AI Models
- Ensuring Fairness and Transparency
- Legal and Ethical Implications
- AI Deployment and Production
- Model Deployment Strategies
- Using Flask and FastAPI for Deployment
- Monitoring and Maintaining AI Models
.png)
TensorFlow
.png)
PyTorch
.png)
Keras
.png)
Scikit-learn
.png)
Apache Spark MLlib
.png)
.png)
.png)
.png)
.png)
.png)
.png)
Contact Us
