Neural Networks and Deep Learning
This course was provided by deeplearning.ai. In this course, I learnt the foundations of deep learning:
- Understood the major technology trends driving Deep Learning.
- Learnt to build, train and apply fully connected deep neural networks.
- Learnt how to implement efficient (vectorized) neural networks.
- Understood the key parameters in a neural network’s architecture.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
This course was provided by deeplearning.ai. In this course, I learnt how to use TensorFlow to implement the deep learning models:
- Studied best practices for using TensorFlow, a popular open-source machine learning framework.
- Learnt to build basic neural networks in TensorFlow.
- Trained neural networks for computer vision applications.
- Understood how to use convolutions to improve the neural network.
Sequences, Time Series and Prediction
This course was provided by Coursera. In this course, I learnt about Sequence, Time Series and Prediction:
- Learnt to solve time series and forecasting problems using TensorFlow.
- Learnt to prepare data for time series learning using best practices.
- Exploreed how RNNs and ConvNets can be used for predictions.
- Built a sunspot prediction model using real-world data.
Introduction to Data Science in Python
This course was provided by the University of Michigan. In this course, I learnt data manipulation and cleaning techniques using the popular python pandas data science library:
- Understood techniques such as lambdas and manipulating CSV files.
- Studied common Python functionality and features used for data science.
- Learnt to query DataFrame structures for cleaning and processing.
- Studied distributions, sampling, and t-tests.
By the end of this course, I was be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
Understanding Research Methods
This course was provided by University of London. In this course, I learnt about the Fundamentals of Doing Research:
- Understood what makes a good Research Question.
- Learnt what is a Literature Review and why do we need to do one.
- Importance of Planning and Management Skills for Research.
Facial Expression Recognition with Keras
This project was authorized by Rhyme. In this project, I have build and train a convolutional neural network (CNN) in Keras from scratch to recognize facial expressions:
- Develop a facial expression recognition model in Keras.
- Build and train a convolutional neural network (CNN).
- Deploy the trained model to a web interface with Flask.
- Apply the model to real-time video streams and image data.
AI For Everyone
This course was provided by deeplearning.ai. In this course, I learnt the non-technical aspects Artificial Intelligence and Machine Learning:
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science.
- What AI realistically can–and cannot–do.
- How to work with an AI team and build an AI strategy.
- How to navigate ethical and societal discussions surrounding AI.