Data Science Prerequisites: The Numpy Stack in Python (V2+)

  • Welcome and Logistics
  • Introduction and Outline
Introduction and Outline
07:50
Extra Resources
03:27
Numpy Section Introduction
05:28
Arrays vs Lists
12:40
Dot Product
07:02
Speed Test
02:55
Matrices
14:45
Solving Linear Systems
03:38
Generating Data
14:32
Numpy Exercise
01:05
Where to Learn More Numpy
06:55
Suggestion Box
03:10
Matplotlib Section Introduction
02:39
Line Chart
03:50
Scatterplot
04:31
Histogram
02:26
Plotting Images
07:40
Matplotlib Exercise
01:40
Where to Learn More Matplotlib
13:10
Pandas Section Introduction
01:17
Loading in Data
03:52
Selecting Rows and Columns
09:48
The apply() Function
02:32
Plotting with Pandas
02:46
Pandas Exercise
02:10
Where to Learn More Pandas
04:25
Scipy Section Introduction
01:25
PDF and CDF
03:07
Convolution
04:34
Scipy Exercise
01:03
Where to Learn More Scipy
07:47
Exercises
08:56
What if I don't meet the math prerequisites?
13:56
What is Machine Learning? (Scikit-Learn Tutorial)
42:14
Machine Learning: Section Introduction
07:47
What is Classification?
12:22
Classification in Code
14:39
What is Regression?
12:14
Regression in Code
08:30
What is a Feature Vector
06:49
Machine Learning is Nothing but Geometry
04:50
All Data is the Same
05:23
Comparing Different Machine Learning Models
09:47
Machine Learning and Deep Learning: Future Topics
05:55
Machine Learning: Section Summary
05:47
When should I use Support Vector Machines?
11:16
What Level of Probability for Machine Learning?
10:30
What is Computer Vision? Guide to learning path for how to master Computer Vision with Deep Learning
15:05
What's this course about? How can you succeed? What should you know first?
06:08
Where to get the code (and how to install libraries)
03:50
Python 2 or Python 3?
01:09
Lists vs. Arrays
06:23
Dot product 1: For loop vs. cosine method vs. dot function
05:13
Dot product 2: Speed comparison
01:38
Vectors and Matrices
04:00
Generating Matrices to Work With
03:27
Matrix Products
02:39
More Matrix Operations
06:55
Solving a Linear System
02:29
Word Problem
01:48
Manual Data Loading
04:33
DataFrames
03:22
More about DataFrames: Selecting Rows and Columns
04:34
Even More about DataFrames: Column Names
03:22
The apply() Function
03:14
Joins
02:33
.values vs. .as_matrix()
07:10
Line Chart
03:20
Scatterplot
03:07
Histogram
03:04
Plotting Images
05:19
Gaussian PDF and CDF
05:01
Sampling from a Gaussian Distribution (1-D)
01:47
Sampling from a Gaussian Distribution (Spherical and Axis-aligned Elliptical)
03:09
Sampling from a General Multivariate Normal
03:03
Other Interesting Scipy Functions
05:55
What is the Appendix?
02:48
Should you code along?
11:00
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
Proof that using Jupyter Notebook is the same as not using it
12:29
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Python 2 vs Python 3
04:38
Where to get discount coupons and FREE deep learning material
05:49
Introduction and Outline
07:50
Extra Resources
03:27
Numpy Section Introduction
05:28
Arrays vs Lists
12:40
Dot Product
07:02
Speed Test
02:55
Matrices
14:45
Solving Linear Systems
03:38
Generating Data
14:32
Numpy Exercise
01:05
Where to Learn More Numpy
06:55
Suggestion Box
03:10
Matplotlib Section Introduction
02:39
Line Chart
03:50
Scatterplot
04:31
Histogram
02:26
Plotting Images
07:40
Matplotlib Exercise
01:40
Where to Learn More Matplotlib
13:10
Pandas Section Introduction
01:17
Loading in Data
03:52
Selecting Rows and Columns
09:48
The apply() Function
02:32
Plotting with Pandas
02:46
Pandas Exercise
02:10
Where to Learn More Pandas
04:25
Scipy Section Introduction
01:25
PDF and CDF
03:07
Convolution
04:34
Scipy Exercise
01:03
Where to Learn More Scipy
07:47
Exercises
08:56
What if I don't meet the math prerequisites?
13:56
What is Machine Learning? (Scikit-Learn Tutorial)
42:14
Machine Learning: Section Introduction
07:47
What is Classification?
12:22
Classification in Code
14:39
What is Regression?
12:14
Regression in Code
08:30
What is a Feature Vector
06:49
Machine Learning is Nothing but Geometry
04:50
All Data is the Same
05:23
Comparing Different Machine Learning Models
09:47
Machine Learning and Deep Learning: Future Topics
05:55
Machine Learning: Section Summary
05:47
When should I use Support Vector Machines?
11:16
What Level of Probability for Machine Learning?
10:30
What is Computer Vision? Guide to learning path for how to master Computer Vision with Deep Learning
15:05
What's this course about? How can you succeed? What should you know first?
06:08
Where to get the code (and how to install libraries)
03:50
Python 2 or Python 3?
01:09
Lists vs. Arrays
06:23
Dot product 1: For loop vs. cosine method vs. dot function
05:13
Dot product 2: Speed comparison
01:38
Vectors and Matrices
04:00
Generating Matrices to Work With
03:27
Matrix Products
02:39
More Matrix Operations
06:55
Solving a Linear System
02:29
Word Problem
01:48
Manual Data Loading
04:33
DataFrames
03:22
More about DataFrames: Selecting Rows and Columns
04:34
Even More about DataFrames: Column Names
03:22
The apply() Function
03:14
Joins
02:33
.values vs. .as_matrix()
07:10
Line Chart
03:20
Scatterplot
03:07
Histogram
03:04
Plotting Images
05:19
Gaussian PDF and CDF
05:01
Sampling from a Gaussian Distribution (1-D)
01:47
Sampling from a Gaussian Distribution (Spherical and Axis-aligned Elliptical)
03:09
Sampling from a General Multivariate Normal
03:03
Other Interesting Scipy Functions
05:55
What is the Appendix?
02:48
Should you code along?
11:00
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
Proof that using Jupyter Notebook is the same as not using it
12:29
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Python 2 vs Python 3
04:38
Where to get discount coupons and FREE deep learning material
05:49

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