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Data Science and Machine Learning roles are some of the most in-demand jobs in the world because of the rapid growth of the machine learning industry with a projected valuation of $400 billion by 2023. However, there is still a huge shortage of competent individuals to satisfy the growing demands for data scientists.

This course hopes to lend a hand in bridging the gap between the demand and the supply of data scientists.

By taking this course, you will gain the skills necessary to become a competent deep learning engineer. You will understand deep learning and train a simple neural network to identify objects in images.

- Understand forward pass computation, the backward pass (backpropagation), Loss functions, and output functions of Deep Neural Networks.
- Explore model optimization techniques such Stochastic Gradient Descent, Adam and RMSProp.
- Understand model evaluation techniques such as MAE, MSE, Accuracy, Precision, Recall and AUC.
- Become introduced to Computer Vision techniques and architectures such as AlexNet, GoogleNet, VGG and Resnet.
- Use Transfer Learning and pre-trained architecture to train an image recognition system.

- Be comfortable with math concepts such as Linear Algebra, Geometry and Calculus.
- Be comfortable with statistical concepts such as probabilities, mean, median and variance
- Have experience with python or be willing to learn quickly.

- Introduction to Machine Learning
- Understand classification and regression algorithms
- Understand the Perceptron algorithm
- Deep Learning Project Pipeline

- The forward pass computation
- The backward pass (backpropagation)
- Loss functions
- Output functions

- Gradient Descent
- Variants of Gradient Descent

- Mean Squared and Mean Absolute Errors
- Confidence Interval
- Accuracy
- Area under Curve (AUC)
- Confusion Matrix with Precision, Recall, and F-score

- Vision operations such as Convolution, Pooling, Dropout
- Vision Project Pipeline
- Vision Architectures (AlexNet, GoogleNet, VGG, Resnet)
- Transfer Learning

- Set up GCP account with PyTorch Image
- Use transfer learning to train an image classifier
- Bonus: Deploy our model to a web page and test it