This Advanced Deep Learning and Computer Vision course cover real applications of computer vision, generative-adversarial networks (GANs).
Introduction to Machine Learning
Parametric/Non-Parametric Learning Algorithm’s
- Logistic Regression
- Classification trees
- Random Forest
- Clustering – K Means & Hierarchical clustering
- Time Series Analysis
- ARIMA Models
- Support Vector Machines
- Model Validation/cross-validation techniques parameter tuning
- Model evolution metrics, MSE, RMSE, R-Square, Adjusted R-Square
- Confusion Matrix, Bias and Variance
- Underfitting overfitting
- Classification
- Decision Tree
- Random Forest
- Clustering
- Time Series Analysis
Introduction to TensorFlow
Recurrent Neural Networks (RNN)
Autoencoders
Supervised and unsupervised ML
Machine Learning Modules
Real-World Data Science & ML case Studies in Python
Introduction to Deep Learning
Convolutional Neural Networks (CNN)
Unsupervised Learning
This Advanced Deep Learning and Computer Vision course cover real applications of computer vision, generative-adversarial networks (GANs), distributed and parallel computing with GPUs, and deployment of deep learning models on the cloud.
