Deep Learning Prerequisites: The Numpy Stack in Python V2

Deep Learning Prerequisites: The Numpy Stack in Python V2, available at Free, has an average rating of 4.63, with 27 lectures, 3 quizzes, based on 3116 reviews, and has 64850 subscribers.

You will learn about Basic operations in Numpy, Scipy, Pandas, and Matplotlib Vector, Matrix, and Tensor manipulation Visualizing data Reading, writing, and manipulating DataFrames This course is ideal for individuals who are Anyone who wants to implement Machine Learning algorithms It is particularly useful for Anyone who wants to implement Machine Learning algorithms.

Enroll now: Deep Learning Prerequisites: The Numpy Stack in Python V2

Summary

Title: Deep Learning Prerequisites: The Numpy Stack in Python V2

Price: Free

Average Rating: 4.63

Number of Lectures: 27

Number of Quizzes: 3

Number of Published Lectures: 27

Number of Published Quizzes: 3

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Basic operations in Numpy, Scipy, Pandas, and Matplotlib
Vector, Matrix, and Tensor manipulation
Visualizing data
Reading, writing, and manipulating DataFrames

Who Should Attend

Anyone who wants to implement Machine Learning algorithms

Target Audiences

Anyone who wants to implement Machine Learning algorithms

Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2).

The reason I made this course is because there is a huge gap for many students between machine learning “theory” and writing actual code.

As I’ve always said: “If you can’t implement it, then you don’t understand it”.

Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer.

This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.

The goal is that, afteryou take this course, you will learn about machine learning algorithms, and implement those algorithms in code using the tools and techniques you learned in this course.

Suggested Prerequisites:

linear algebra
probability
Python programming

Course Curriculum

Chapter 1: Welcome and Logistics

Lecture 1: Introduction and Outline

Lecture 2: Extra Resources

Chapter 2: Numpy

Lecture 1: Numpy Section Introduction

Lecture 2: Arrays vs Lists

Lecture 3: Dot Product

Lecture 4: Speed Test

Lecture 5: Matrices

Lecture 6: Solving Linear Systems

Lecture 7: Generating Data

Lecture 8: Numpy Exercise

Chapter 3: Matplotlib

Lecture 1: Matplotlib Section Introduction

Lecture 2: Line Chart

Lecture 3: Scatterplot

Lecture 4: Histogram

Lecture 5: Plotting Images

Lecture 6: Matplotlib Exercise

Chapter 4: Pandas

Lecture 1: Pandas Section Introduction

Lecture 2: Loading in Data

Lecture 3: Selecting Rows and Columns

Lecture 4: The apply() Function

Lecture 5: Plotting with Pandas

Lecture 6: Pandas Exercise

Chapter 5: Scipy

Lecture 1: Scipy Section Introduction

Lecture 2: PDF and CDF

Lecture 3: Convolution

Lecture 4: Scipy Exercise

Chapter 6: Appendix / FAQ

Lecture 1: BONUS

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 26 votes
2 stars: 41 votes
3 stars: 229 votes
4 stars: 912 votes
5 stars: 1909 votes

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