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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