In this repository I will put links for Deep Learning/Machine Learning resources that I find online as I learn more about the subject. I will also try to attach a Colab reference on each of my iPython notebooks so the work can be reproduced later. Some examples will come from the books as I use them for reference.

• ML-Algos covers fundamental machine learning algorithms and provides resources to help you refresh some basic machine learning concepts. If you feel like you require some refreshers on ML it is a good idea to check out ML-Algos which uses Python's Scikit-Learn to cover some essential machine learning concepts.

• Hands-On Machine Learning with Scikit-Learn and TensorFlow
• Deep Learning with Python

One good thing about Coursera is they provide a great financial aid application if you can't afford the course make sure you click on their financial aid application. Coursera has a strong financial aid program, so you can essentially get certificates for the entire Deep Learning specialization for cheap.

• Neural Networks and Deep Learning
• Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
• Structuring Machine Learning Projects
• Convolutional Neural Networks
• Sequence Models

• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

• Intro to TensorFlow for Deep Learning

• Kaggle Python overview (with Pandas, Numpy, and Matplotlib)
• Python Visualization

• Programming Convolutions with Keras (with a visualization of Convolutions and Pooling)
• Visualizing Convolutions and Pooling with different filters
• Keras' ImageGenerator with visualization
• Keras ImageDataGenerator loading with validation data
• Debugging Tensorflow
• Image Augmentation with Keras