Online Python Programming For AI/ML (Advanced) Course

1. Mastery in Python for AI/ML Development

• Gain deep knowledge of Python libraries like TensorFlow, PyTorch, and Keras.
• Learn advanced data structures, algorithms, and OOP for efficient model implementation.
• Build complex AI/ML projects from scratch, enhancing real-world application skills.

2. Comprehensive Understanding of Machine Learning Algorithms

• Learn both supervised and unsupervised learning algorithms in depth.
• Work with real datasets to implement decision trees, neural networks, and SVM.
• Fine-tune models using techniques like hyperparameter tuning and cross-validation.

3. Expertise in Data Preprocessing and Feature Engineering

• Handle missing data, outliers, and perform scaling for better model accuracy.
• Learn advanced feature extraction and selection techniques to improve performance.
• Master data augmentation, especially useful in image and text-based models.

4. Proficiency in Deep Learning Techniques

• Gain in-depth knowledge of neural networks, CNNs, RNNs, and LSTMs.
• Work on advanced use cases like image classification, natural language processing, and more.
• Understand backpropagation and gradient descent optimization for neural network training.

5. Hands-on Experience with AI/ML Project Deployment

• Learn how to deploy models in real-world environments (e.g., cloud, mobile).
• Use tools like Flask, Docker, and REST APIs to build and deploy AI/ML services.
• Understand the nuances of scaling AI/ML models for high-traffic production systems.

6. Skill Development in Model Evaluation and Optimization

• Learn evaluation metrics like precision, recall, F1-score, and AUC-ROC.
• Implement model optimization techniques such as regularization, dropout, and batch normalization.
• Practice model interpretability with SHAP and LIME to ensure transparency and trust.