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Updated in [March 06th, 2023]

This course, Deep Learning Prerequisites: The Numpy Stack in Python V2, provides an overview of the fundamental operations of Numpy, Scipy, Pandas, and Matplotlib. Participants will learn how to manipulate vectors, matrices, and tensors, as well as how to visualise data. Additionally, participants will gain an understanding of how DataFrames can be read, written, and manipulated.

[Applications]
After completing this course, students can apply their knowledge of the Numpy Stack in Python V2 to a variety of tasks. They can use Numpy to perform mathematical operations on vectors, matrices, and tensors. They can use Scipy to perform scientific computing tasks such as optimization, integration, and interpolation. They can use Pandas to read, write, and manipulate data in DataFrames. Finally, they can use Matplotlib to create data visualizations. With these skills, students can create powerful data analysis pipelines and build sophisticated machine learning models.

[Career Paths]
1. Data Scientist: Data Scientists use a variety of tools and techniques to analyze data and develop insights that can be used to inform business decisions. They use a combination of mathematics, statistics, machine learning, and deep learning to uncover patterns and trends in data. Data Scientists are in high demand as businesses increasingly rely on data-driven decisions.

2. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use a variety of tools and techniques to build and optimize models, including deep learning. They also need to be able to interpret the results of their models and communicate them to stakeholders.

3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying AI-based solutions. They use a variety of tools and techniques to build and optimize AI models, including deep learning. They also need to be able to interpret the results of their models and communicate them to stakeholders.

4. Data Analyst: Data Analysts use a variety of tools and techniques to analyze data and develop insights that can be used to inform business decisions. They use a combination of mathematics, statistics, and machine learning to uncover patterns and trends in data. Data Analysts are in high demand as businesses increasingly rely on data-driven decisions.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and deep learning. With the increasing demand for data-driven solutions, this degree path is becoming increasingly popular.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence systems and their applications. It covers topics such as machine learning, deep learning, natural language processing, computer vision, and robotics. This degree path is becoming increasingly popular as AI technology is becoming more widely used in various industries.

3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, and deep learning. This degree path is becoming increasingly popular as data-driven solutions are becoming more widely used in various industries.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of machine learning algorithms and their applications. It covers topics such as deep learning, natural language processing, computer vision, and robotics. This degree path is becoming increasingly popular as machine learning technology is becoming more widely used in various industries.

Course Syllabus

Numpy Section Introduction

Arrays vs Lists

Dot Product

Speed Test

Matrices

Solving Linear Systems

Generating Data