Description

This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.

Welcome to the course where you will learn about the NumPy stack in Python, which is an important prerequisite for deep learning, machine learning, and data science. In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. This involves performing numerical computation and representing data, visualizing data with plots, loading in, and manipulating data using DataFrames, performing statistics and probability, and building machine learning models for classification and regression. In this course, we will first start with NumPy; we will understand the benefits of NumPy array and then we will look at some complicated matrix operations, such as products, inverses, determinants, and solving linear systems. Then we will cover Matplotlib. In this section, we will go over some common plots, namely the line chart, scatter plot, and histogram. We will also look at how to show images using Matplotlib. Next, we will talk about Pandas. We will look at how much easier it is to load a dataset using Pandas versus trying to do it manually. Then we will look at some data frame operations useful in machine learning, such as filtering by column, filtering by row, and the apply function. Later, you will learn about SciPy. In this section, you will learn how to do common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing. Finally, we will also cover some basics of machine learning that will help us start our deep learning journey. By the end of the course, we will be able to confidently use the NumPy stack in deep learning and data science.

What You Will Learn

Understand supervised machine learning with real-world examples
Understand and code using the NumPy stack
Make use of NumPy, SciPy, Matplotlib, and Pandas to implement numerical algorithms
Understand the pros and cons of various machine learning models
Get a brief introduction to the classification and regression
Learn how to calculate the PDF and CDF under the normal distribution

Audience

This course is designed for anyone who is interested in data science and machine learning, who knows Python and wants to take the next step into Python libraries for data science, or who is interested in acquiring tools to implement machine learning algorithms.

One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course.

Approach

You will learn how to use NumPy, Matplotlib, Pandas, and SciPy to carry out crucial data science and machine learning tasks in this self-paced course. The course is well-balanced with both theoretical and practical coding exercises. Each section, we first cover the theory concept and demonstrate it using a real-world example for better understanding.

Key Features

Study basics of machine learning and understand how to use the NumPy stack for deep learning in data science * Learn how to use NumPy, Matplotlib, Pandas, and SciPy for critical tasks in data science and machine learning * Perform numerical computations, visualize data, load, and manipulate datasets using Pandas

About the Author

Lazy Programmer

The Lazy Programmer is an AI/ML engineer focusing on deep learning with experience in data science, big data engineering, and full-stack development. With a background in computer engineering and specialization in ML, he holds two master's degrees in computer engineering and statistics with finance applications. His online advertising and digital media expertise include data science and big data. He has created DL models for prediction and has experience in recommender systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He is a web programmer with experience in Python, Ruby/Rails, PHP, and Angular.

Course Outline

1. Welcome and Logistics

Welcome to the course! In this section, we will get introduced to the course goals.

1. Introduction and Outline

In this video, we will get introduced to the course and understand the learning objective.

2. NumPy

In this section of the course, we will dive into the world of NumPy, a powerful numerical computing library in Python that is widely used in data science and machine learning.

1. NumPy Section Introduction

In this video, we will have a quick overview of NumPy.

2. Arrays Versus Lists

In this video, we will be comparing NumPy array to Python list.

3. Dot Product

In this video, we will discuss Dot Product in NumPy.

4. Speed Test

In this video, we will do a speed test to measure how much faster NumPy is than regular Python list in terms of the dot product.

5. Matrices

In this video, you will learn about matrices.

6. Solving Linear Systems

In this video, we will work on solving an example problem to understand linear systems.

7. Generating Data

In this video, we will discuss about generatingabout how to generate data.

8. NumPy Exercise

In this video, we will look at the NumPy exercise that must be resolved on our own.

9. Where to Learn More NumPy

In this video, we will understand why we need NumPy and how it is applied in more advanced scenarios.

3. Matplotlib

In this section, we will talk about Matplotlib.

1. Matplotlib Section Introduction

In this video, we will have a quick overview of Matplotlib that is used to visualize data and see what we are going to cover in this section.

2. Line Chart

In this video, you will learn about a line chart.

3. Scatterplot

In this video, you will learn about a scatterplot.

4. Histogram

In this video, you will learn about a histogram.

5. Plotting Images

In this video, you will learn how to plot images.

6. Matplotlib Exercise

In this video, we will have a look at the Matplotlib exercise that needs to be resolved on our own.

7. Where to Learn More Matplotlib

In this video, we will understand from where we can learn more about Matplotlib.

4. Pandas

In this section, we will talk about Pandas.

1. Pandas Section Introduction

In this video, we will be introduced to Pandas and understand this section''s learning objective.

2. Loading in Data

In this video, you will learn how to load in data using Pandas.

3. Selecting Rows and Columns

In this video, you will learn how to select rows and columns from a data frame.

4. The apply() Function

In this video, you will learn about the apply() function.

5. Plotting with Pandas

In this video, you will learn how to plot with Pandas.

6. Pandas Exercise

In this video, we will have a look at the Pandas exercise that needs to be resolved on our own.

7. Where to Learn More Pandas

In this video, we will understand from where we can learn more about Pandas.

5. SciPy

In this section, we will talk about SciPy.

1. SciPy Section Introduction

In this video, we will be introduced to SciPy and understand this section''s learning objective.

2. PDF and CDF

In this video, you will learn how to calculate PDF and CDF under normal distribution.

3. Convolution

In this video, you will learn how to apply convolution to an image to create a blurring filter.

4. SciPy Exercise

In this video, we will have a look at the SciPy exercise that needs to be resolved on our own.

5. Where to Learn More SciPy

In this video, we will summarize what we have learnt about SciPy and understand where it can be applied.

6. Machine Learning Basics

In this section, we will cover machine learning basics.

1. Machine Learning: Section Introduction

In this video, we will understand what machine learning is and the learning objective of this section.

2. What Is Classification?

In this video, we will understand classification.

3. Classification in Code

In this video, we will practice all that we have learnt in the previous video about classification.

4. What Is Regression?

In this video, we will understand what regression is.

5. Regression in Code

In this video, we will practice all that we have learnt in the previous video about regression.

6. What Is a Feature Vector?

In this video, you will learn about a feature vector.

7. Machine Learning Is Nothing but Geometry.

In this video, we will understand that machine learning is nothing but a geometry problem and see how it works for classification and regression.

8. All Data Is the Same

In this video, we will understand how all data is the same as all machine learning interfaces are the same.

9. Comparing Different Machine Learning Models

In this video, we will understand how all machine learning interfaces are the same.

10. Machine Learning and Deep Learning: Future Topics

In this video, you will learn what other types of machine learning exist and some future topics that we can look forward to learning.

11. Machine Learning: Section Summary

In this video, we will summarize our learning from this section.

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Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python

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