- Statistical Analysis
- Data Cleaning and Preprocessing
- Data Visualization
- Data Wrangling
- Programming (Python/R)
- Database Management (SQL)
- Machine Learning Basics
- Data Ethics and Privacy
- Communication Skills
- Critical Thinking
- Programming Languages (Python)
- Mathematics and Statistics (Linear Algebra, Calculus, Probability)
- Supervised Learning (Regression, Classification)
- Unsupervised Learning (Clustering, Dimensionality Reduction)
- Deep Learning (Neural Networks, CNNs, RNNs)
- Model Evaluation and Validation
- Feature Engineering
- Hyperparameter Tuning
- Ensemble Methods
- Model Deployment
- Machine Learning Ethics
- Interpretability
- Time Series Analysis
- Natural Language Processing (NLP)
- Neural Network Architecture Design
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Deep Learning Frameworks (e.g., TensorFlow, PyTorch)
- Transfer Learning
- Computer Vision with Deep Learning
- Natural Language Processing (NLP) with Deep Learning
- Generative Adversarial Networks (GANs)
- Autoencoders
- Hyperparameter Tuning for Deep Learning Models
- Model Interpretability in Deep Learning
- TensorFlow/Keras or PyTorch for Deep Learning
- Deep Learning for Time Series Analysis
- Image Preprocessing Techniques
- Object Detection
- Image Classification
- Image Segmentation
- Feature Extraction
- Convolutional Neural Networks (CNNs)
- Transfer Learning for Computer Vision
- OpenCV (Computer Vision Library)
- Image Augmentation
- Face Detection and Recognition