## Course content

- Introduction to Research
- Introduction to Machine Learning

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# Tag: Machine Learning Basics

## Machine Learning For Researchers

## Course content

## Deep Learning Prerequisites: The Numpy Stack in Python (V2+)

## Course content

## Deep Learning A-Z 2023: Neural Networks, AI & ChatGPT Prize

## Course content

- Introduction to Research
- Introduction to Machine Learning

- Welcome and Logistics
- Numpy (New)
- Matplotlib (New)
- Pandas (New)
- Scipy (New)
- Bonus Exercises
- Beginner Troubleshooting
- Machine Learning Basics
- Setting Up Your Environment (FAQ by Student Request)
- Extra Help With Python Coding for Beginners (FAQ by Student Request)

- Welcome to the course!
- Part 1 – Artificial Neural Networks
- ANN Intuition
- Building an ANN
- Part 2 – Convolutional Neural Networks
- CNN Intuition
- Building a CNN
- Part 3 – Recurrent Neural Networks
- RNN Intuition
- Building a RNN
- Evaluating and Improving the RNN
- Part 4 – Self Organizing Maps
- SOMs Intuition
- Building a SOM
- Mega Case Study
- Part 5 – Boltzmann Machines
- Boltzmann Machine Intuition
- Building a Boltzmann Machine
- Part 6 – AutoEncoders
- AutoEncoders Intuition
- Building an AutoEncoder
- Annex – Get the Machine Learning Basics
- Regression & Classification Intuition
- Data Preprocessing
- Data Preprocessing in Python
- Logistic Regression
- Congratulations!! Don’t forget your Prize