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Name: Arcada UAS

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Arcada UAS's Projects

introduction-to-convolutional-neural-networks-with-pytorch

Use CNNs to solve image classification problems using PyTorch. Improve your model's performance by applying data augmentation and batch normalization.

introduction-to-data-analytics-with-pandas-and-numpy

Learn to use NumPy to perform statistics and speed up matrix computations as well as visualize data by constructing, modifying, and interpreting histograms and scatter plots. Discover how to generate and interpret statistical models using pandas and statsmodels and solve real-world problems using data analytics techniques.

introduction-to-data-storage

In this module, you will explore the broad range of capabilities of AI, and see some of the fields that it is changing. You will build your first AI system, and look at optimization.

introduction-to-data-storage-on-cloud-services-aws

Here you will cover the pros and cons of various cloud data storage solutions. You will create, access, and manage your Amazon S3 cloud services. Learn how to use the AWS Command Line Interface (CLI) and Python Software Development Kit (SDK) to control Amazon Web Services (AWS). Lastly, you will create a simple data pipeline that reads from and writes to your cloud data storage.

introduction-to-data-system-design

You will look at some existing system designs and analyze the reasons for specific design choices. The module will also cover how to design AI systems with some cases from designing general-purpose data storage systems too.

introduction-to-data-wrangling-with-python

Briefly review the foundational components of data wrangling and Python data structures.

introduction-to-decision-trees

This chapter introduces you to two types of supervised learning algorithms in detail. The first algorithm will help us to classify data points using decision trees, while the other algorithm will help us classify using random forests.

introduction-to-deep-learning-and-neural-networks

In this chapter you will be introduced to the final topic on neural networks and deep learning. You will come across TensorFlow, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). You will also be implementing an image classification program using neural networks and deep learning

introduction-to-deep-learning-and-pytorch

Explore some of the most popular applications of deep learning, understand what PyTorch is, and use PyTorch to build a simple single-layer network.

introduction-to-deep-q-learning

The module covers different Q learning algorithms using Open AI and FrozenLake.

introduction-to-dynamic-programming

The module introduces dynamic programming using an example of coin-exchange. Then we go over to how and why it is used in Reinforcement Learning. The module also covers classic dynamic programming algorithms.

introduction-to-image-processing

Study the components of image processing, and practice accessing and manipulating pixels in OpenCV and Matplotlib.

introduction-to-jupyter-notebooks

This course introduces the basic functions and features of Jupyter Notebooks, as well as major Python libraries.

introduction-to-machine-learning-models

Learn to compare, contrast, and apply different types of machine learning algorithm. Also analyze overfitting and implement regularization and solve real-world problems using the machine learning algorithms.

introduction-to-monte-carlo-methods

This course examines the Monte Carlo methods and its types and solves the frozen lake problem with Monte Carlo methods.

introduction-to-neural-networks-and-deep-learning

This module covers the basics components of a neural network and its essential operations. It also explores a trained neural network created using TensorFlow

introduction-to-numpy-pandas-and-matplotlib

Implement advanced operations and data handling techniques on essential Python libraries to perform statistical descriptive analysis

introduction-to-policy-based-methods-for-reinforcement-learning

This module looks at policy based methods of reinforcement learning, principally the drawbacks to value based methods like Q learning that motivate the use of policy gradients.

introduction-to-python-math-strings-conditionals-and-loops

Dive into Python by getting an understanding of the core elements, keywords and syntax. Start writing Python programs by assigning variables, applying functions and combining math operations.

introduction-to-python-structures-and-tools

Review the basic Python tools and data structures before applying your new skills by using loops, functions, lists and more to solve problems.

introduction-to-regression

In this module you will be introduced to regression which plays an important role while it comes to prediction of the future by using the past historical data. You will come across various techniques such as Linear regression with one and multiple variables, along with polynomial and Support Vector Regression

introduction-to-reinforcement-learning

This module introduces the world of reinforcement learning and discusses some common applications. You will solve an autonomous driving problem using pure Python

introduction-to-scikit-learn

This module covers scikit-learn's syntax to solve simple data problem, which will be the starting point to develop machine learning solutions

introduction-to-supervised-learning-with-python

Discover the key concepts of supervised learning and learn to load, manipulate and describe data with key Python packages.

introduction-to-temporal-difference-learning

This module introduces temporal-difference learning and focuses on how it develops over the ideas of both Monte Carlo methods, and dynamic programming.

introduction-to-the-ethics-of-ai-data-storage

You will look at several case studies, examining everything from AI being used to manipulate elections, to AI displaying racial and sexist prejudices. Implement a simple sentiment classifier to differentiate between positive and negative words and sentences. You'll observe how this works in many cases, and display the problematic biases and human stereotypes in the classifier.

introduction-to-workflow-management-platform-airflow

In this module, you will look at creating a pipeline by breaking down a job into multiple executable stages. You will implement a simple linear pipeline and then move further by implementing a multi-stage data pipeline, then automate the multi-stage pipeline using Bash. Further to this you will improve the efficiency by running the pipeline as an asynchronous process using the ETL workflow and then create DAG for the pipeline and implement it using Airflow.

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