AI Programming with Python Bootcamp
Bootcamp AI
About This Course
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Learning Objectives
Learn the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).
Requirements
- Basic computer skills like managing files, navigating the Internet, and running programs will be useful.
- Basic algebra, and programming knowledge in any language.
- Hardware Requirements Computer running OS X or Windows.
Curriculum
453 Lessons
Welcome to AI Programming with Python
Start using AI techniques and developing skills related to programming, linear algebra, and neural networks.
Why Python Programming
Start coding with Python, drawing upon libraries and automation scripts to solve complex problems quickly.
Data Types and Operators
01. Introduction00:1:37Preview
02. Arithmetic Operators00:2:49Preview
03. Quiz: Arithmetic Operators00:00:00Preview
03.2 Quiz: Arithmetic Operators00:00:00Preview
03.2 Quiz: Arithmetic Operators
04. Solution: Arithmetic OperatorsPreview
05. Variables and Assignment Operators00:1:34Preview
05.2 Variables and Assignment Operators00:1:56Preview
05.3 Variables and Assignment Operators00:00:00Preview
06. Quiz: Variables and Assignment Operators00:00:00Preview
06.2 Quiz: Variables and Assignment OperatorsPreview
06.2 Quiz: Variables and Assignment Operators
07. Solution: Variables and Assignment Operators
08. Integers and Floats3:54
08.2 Integers and Floats2:24
09. Quiz: Integers and Floats00:00:00
09. Quiz: Integers and Floats
10. Booleans, Comparison Operators, and Logical Operators2:29
11. Quiz: Booleans, Comparison Operators, and Logical Operators00:00:00
11. Quiz: Booleans, Comparison Operators, and Logical Operators
12. Solution: Booleans, Comparison and Logical Operators
13. Strings4:20
14. Quiz: Strings00:00:00
14.2 Quiz: Strings00:00:00
14. Quiz: Strings
14.3 Quiz: Strings00:00:00
14.3 Quiz: Strings
15. Solution: Strings
16. Type and Type Conversion2:29
17. Quiz: Type and Type Conversion
17. Quiz: Type and Type Conversion00:00:00
18. Solution: Type and Type Conversion
19. String Methods2:43
20. String Methods
20. String Methods00:00:00
20.2 String Methods00:00:00
21. Another String Method – Split
22. Lists and Membership Operators3:04
22.2 Lists and Membership Operators2:11
22.3 Lists and Membership Operators2:11
23. Quiz: Lists and Membership Operators00:00:00
23.2 Quiz: Lists and Membership Operators00:00:00
23. Quiz: Lists and Membership Operators
24. Solution: List and Membership Operators
25. List Methods00:00:00
25.2 List Methods00:00:00
26. Quiz: List Methods
26. Quiz: List Methods00:00:00
27. Tuples00:00:00
28. Quiz: Tuples
28. Quiz: Tuples00:00:00
29. Sets1:41
30. Quiz: Sets
30. Quiz: Sets00:00:00
31. Dictionaries and Identity Operators00:00:00
32. Quiz: Dictionaries and Identity Operators00:00:00
32. Quiz: Dictionaries and Identity Operators
32.2 Quiz: Dictionaries and Identity Operators00:00:00
33. Solution: Dictionaries and Identity Operators
34. Quiz: More With Dictionaries
34. Quiz: More With Dictionaries
34.2 Quiz: More With Dictionaries00:00:00
35. Compound Data Structures0:56
36. Quiz: Compound Data Structures00:00:00
36. Quiz: Compound Data Structures
37. Solution: Compound Data Structions
38. Conclusion00:00:00
39. Summary
Control Flow
01. Introduction0:40
02. Conditional Statements00:00:00
02.2 Conditional Statements00:00:00
02.3 Conditional Statements00:00:00
02.4 Conditional Statements Practice00:00:00
03. Practice: Conditional Statements00:00:00
04. Solution: Conditional Statements
05. Quiz: Conditional Statements00:00:00
05.2 Quiz: Conditional Statements00:00:00
06. Solution: Conditional Statements
07. Boolean Expressions for Conditions00:00:00
07.2 Boolean Expressions for Conditions00:00:00
07.3 Boolean Expressions for Conditions00:00:00
08. Quiz: Boolean Expressions for Conditions00:00:00
09. Solution: Boolean Expressions for Conditions
10. For Loops00:00:00
11. Practice: For Loops00:00:00
11.2 Practice: For Loops00:00:00
12. Solution: For Loops Practice
13. Quiz: For Loops
13. Quiz: For Loops00:00:00
13.2 Quiz: For Loops00:00:00
13.3 Quiz: For Loops00:00:00
13.4 Quiz: For Loops00:00:00
14. Solution: For Loops Quiz
15. Quiz: Match Inputs To Outputs00:00:00
15. Quiz: Match Inputs To Outputs
16. Building Dictionaries00:00:00
17. Iterating Through Dictionaries with For Loops
17.2 Iterating Through Dictionaries with For Loops00:00:00
18. Quiz: Iterating Through Dictionaries00:00:00
18.2 Quiz: Iterating Through Dictionaries00:00:00
18.3 Quiz: Iterating Through Dictionaries00:00:00
19. Solution: Iterating Through Dictionaries
20. While Loops00:00:00
21. Practice: While Loops00:00:00
21.2 Practice: While Loops00:00:00
22. Solution: While Loops Practice
23. Quiz: While Loops00:00:00
23.2 Quiz: While Loops00:00:00
23.3 Quiz: While Loops00:00:00
24. Solution: While Loops Quiz
25. Break, Continue00:00:00
25.2 Break, Continue00:00:00
26. Quiz: Break, Continue00:00:00
27. Solution: Break, Continue
28. Zip and Enumerate
29. Quiz: Zip and Enumerate00:00:00
29.2 Quiz: Zip and Enumerate00:00:00
29.3 Quiz: Zip and Enumerate00:00:00
29.4 Quiz: Zip and Enumerate00:00:00
29.5 Quiz: Zip and Enumerate00:00:00
30. Solution: Zip and Enumerate
31. List Comprehensions00:00:00
32. Quiz: List Comprehensions00:00:00
32.2 Quiz: List Comprehensions00:00:00
32.3 Quiz: List Comprehensions00:00:00
33. Solution: List Comprehensions
34. Conclusion00:00:00
Functions
01. Introduction1:00
02. Defining Functions00:1:34
02. Defining Functions
02.2 Defining Functions00:00:00
02.3 Defining Functions00:4:04
03. Quiz: Defining Functions00:00:00
03.2 Quiz: Defining Functions00:00:00
04. Solution: Defining Functions
05. Variable Scope1:02
06. Variable Scope
07. Solution: Variable Scope
08. Documentation2:06
09. Quiz: Documentation00:00:00
10. Solution: Documentation
11. Lambda Expressions00:00:00
12. Quiz: Lambda Expressions00:00:00
12.2 Quiz: Lambda Expressions00:00:00
13. Solution: Lambda Expressions
14. [Optional] Iterators and Generators00:00:00
15. [Optional] Quiz: Iterators and Generators00:00:00
15.2 [Optional] Quiz: Iterators and Generators00:00:00
16. [Optional] Solution: Iterators and Generators
17. [Optional] Generator Expressions
18. Conclusion00:00:00
19. Further Learning
Scripting
01. Introduction00:00:00
02. Python Installation
03. Install Python Using Anaconda
04. [For Windows] Configuring Git Bash to Run Python
05. Running a Python Script00:00:00
06. Programming Environment Setup00:00:00
07. Editing a Python Script
07. Editing a Python Script
08. Scripting with Raw Input00:00:00
09. Quiz: Scripting with Raw Input00:00:00
10. Solution: Scripting with Raw Input
11. Errors and Exceptions00:00:00
12. Errors and Exceptions
13. Handling Errors00:00:00
13.2 Handling Errors00:00:00
14. Practice: Handling Input Errors00:00:00
15. Solution: Handling Input Errors
16. Accessing Error Messages
17. Reading and Writing Files00:00:00
17.2 Reading and Writing Files00:00:00
17.3 Reading and Writing Files00:00:00
18. Quiz: Reading and Writing Files
18.2 Quiz: Reading and Writing Files00:00:00
19. Solution: Reading and Writing Files
20. Importing Local Scripts00:00:00
21. The Standard Library00:00:00
22. Quiz: The Standard Library00:00:00
22.2 Quiz: The Standard Library00:00:00
22.2 Quiz: The Standard Library
23. Solution: The Standard Library
24. Techniques for Importing Modules00:00:00
24.2 Techniques for Importing Modules00:00:00
25. Quiz: Techniques for Importing Modules
26. Third-Party Libraries00:00:00
27. Experimenting with an Interpreter00:00:00
28. Online Resources
28. Online Resources
28.2 Online Resources
29. Conclusion00:00:00
Lab Classifying Images
In this project, learners will be testing their newly-acquired Python coding skills by using a trained image
classifier. They will need to use the trained neural network to classify images of dogs (by breeds) and compare the output with the known dog breed classification. Learners will have a chance to build their own functions, use command line arguments, test the runtime of the code, create a dictionary of lists, and more.
01. Instructor
02. Lab Description
03. Lab Instructions1:40
04. Workspace How-to00:00:00
05. Workspaces: Best Practices
06. Lab Workspace
07. Timing Code
07.2 Timing Code00:00:00
08. Command Line Arguments00:00:00
08.2 Command Line Arguments
09. Mutable Data Types and Functions00:00:00
10. Creating Pet Image Labels – Part 1
11. Creating Pet Image Labels – Part 2
11.2 Creating Pet Image Labels – Part 200:00:00
12. Classifying Images – Part 1
13. Classifying Images – Part 2
13.2 Classifying Images – Part 200:00:00
14. Classifying Labels as Dogs
14.2 Classifying Labels as Dogs00:00:00
15. Calculating Results
15.2 Calculating Results00:00:00
16. Printing Results
16.2 Printing Results00:00:00
17. Results
18. Concluding Remarks
19. Lab Solution Workspace
NumPy
Learn how to use all the key tools for working with data in Python: Jupyter Notebooks, NumPy, Anaconda, Pandas,
and Matplotlib.
01. Instructors
02. Introduction to NumPy
03. Why Use NumPy?3:08
04. Creating and Saving NumPy ndarrays00:00:00
05. Using Built-in Functions to Create ndarrays00:00:00
06. Create an ndarray00:00:00
07. Accessing, Deleting, and Inserting Elements Into ndarrays00:00:00
08. Slicing ndarrays00:00:00
09. Boolean Indexing, Set Operations, and Sorting00:00:00
10. Manipulating ndarrays00:00:00
11. Arithmetic operations and Broadcasting00:00:00
12. Creating ndarrays with Broadcasting00:00:00
13. Getting Set Up for the Mini-Project
14. Mini-Project: Mean Normalization and Data Separation
Pandas
01. Instructors
02. Introduction to Pandas
03. Why Use Pandas?
04. Creating Pandas Series00:00:00
05. Accessing and Deleting Elements in Pandas Series00:00:00
06. Arithmetic Operations on Pandas Series00:00:00
07. Manipulate a Series00:00:00
08. Creating Pandas DataFrames00:00:00
09. Accessing Elements in Pandas DataFrames00:00:00
10. Dealing with NaN00:00:00
11. Manipulate a DataFrame00:00:00
12. Loading Data into a Pandas DataFrame00:00:00
13. Getting Set Up for the Mini-Project
14. Mini-Project: Statistics From Stock Data
Matplotlib and Seaborn Part 1
Learn how to use Matplotlib to choose appropriate plots for one and two variables based on the types of data you have.
01. Instructor
02. Introduction00:1:12Preview
03. Tidy Data
04. Bar Charts2:28
04.2 Bar Charts4:13
05. Absolute vs. Relative Frequency1:04
05.2 Absolute vs. Relative Frequency00:00:00
06. Counting Missing Data
07. Bar Chart Practice
08. Pie Charts00:00:00
09. Histograms00:00:00
09.2 Histograms00:00:00
10. Histogram Practice
11. Figures, Axes, and Subplots
11. Figures, Axes, and Subplots
11.2 Figures, Axes, and Subplots
12. Choosing a Plot for Discrete Data
13. Descriptive Statistics, Outliers and Axis Limits00:00:00
13.2 Descriptive Statistics, Outliers and Axis Limits00:00:00
14. Scales and Transformations00:00:00
14.2 Scales and Transformations00:00:00
15. Scales and Transformations Practice
16. Lesson Summary00:00:00
17. Extra Kernel Density Estimation
Matplotlib and Seaborn Part 2
01. Introduction1:05Preview
02. Scatterplots and Correlation2:38
02.2 Scatterplots and Correlation00:00:00
03. Overplotting, Transparency, and Jitter00:00:00
03.2 Overplotting, Transparency, and Jitter00:00:00
04. Heat Maps00:00:00
04.2 Heat Maps00:00:00
05. Scatterplot Practice
06. Violin Plots00:00:00
06.2 Violin Plots00:00:00
07. Box Plots00:00:00
07.2 Box Plots00:00:00
08. Violin and Box Plot Practice
09. Clustered Bar Charts00:00:00
09.2 Clustered Bar Charts00:00:00
10. Categorical Plot Practice
11. Faceting00:00:00
11.2 Faceting00:00:00
12. Adaptation of Univariate Plots00:00:00
12.2 Adaptation of Univariate Plots00:00:00
13. Line Plots00:00:00
13.2 Line Plots00:00:00
14. Additional Plot Practice
15. Lesson Summary00:00:00
16. Postscript Multivariate Visualization
17. Extra Swarm Plots
18. Extra Rug and Strip Plots
Introduction
Learn the foundational math needed for AI success—vectors, linear transformations, and matrices—as well as the linear algebra behind neural networks.
01. Our Goal
02. Instructors
03. Essence of Linear Algebra00:00:00
04. Structure of this lesson
05. Working with Equations
06. Try our workspace out!00:00:00
07. Try our workspace again!00:00:00
Vectors
01. What’s a Vector00:00:00Preview
02. Vectors, what even are they Part 200:00:00
03. Vectors, what even are they Part 300:00:00
04. Vectors- Mathematical definition
05. Transpose
06. Magnitude and Direction
07. Vectors- Quiz 1
08. Operations in the Field
09. Vector Addition
10. Vectors- Quiz 2
11. Scalar by Vector Multiplication
12. Vectors Quiz 3
13. Vectors Quiz Answers
Linear Combination
01. Linear Combination. Part 100:00:00
02. Linear Combination. Part 200:00:00
03. Linear Combination and Span
04. Linear Combination -Quiz 1
05. Linear Dependency
06. Solving a Simplified Set of Equations
07. Linear Combination – Quiz 2
08. Linear Combination – Quiz 3
Linear Transformation and Matrices
01. What is a Matrix
02. Matrix Addition
03. Matrix Addition Quiz
04. Scalar Multiplication of Matrix and Quiz
05. Multiplication of a Square Matrices
06. Square Matrix Multiplication Quiz
07. Matrix Multiplication – General
08. Matrix Multiplication Quiz
09. Linear Transformation and Matrices . Part 100:00:00
10. Linear Transformation and Matrices. Part 200:00:00
11. Linear Transformation and Matrices. Part 300:00:00
12. Linear Transformation Quiz Answers
Vectors Lab
01. Vectors Lab
02. Vectors Lab Solution00:00:00
02.2 Vectors Lab Solution00:00:00
Linear Combination Lab
01. Linear Combination
02. Linear Combination Lab Solution00:00:00
Linear Mapping Lab
01. Lab Description
02. Visualizing Matrix Multiplication
03. Matrix Multiplication Lab
04. Linear Mapping Lab Solution00:00:00
04.2 Linear Mapping Lab Solution00:00:00
04.3 Linear Mapping Lab Solution00:00:00
Linear Algebra in Neural Networks
01. Instructor
02. Brief Introduction
03. What is a Neural Network00:00:00
04. How Are The Neurons Connected
05. Putting The Pieces Together00:00:00
06. The Feedforward Process- Finding h00:00:00
07. The Feedforward Process- Finding y00:00:00
Introduction to Neural Networks
Gain a solid foundation in the latest trends in AI: neural networks, deep learning, and PyTorch.
01. Instructor
02. Introduction00:00:00
03. Classification Problems 100:00:00
03. Classification Problems 1
04. Classification Problems 200:00:00
05. Linear Boundaries00:00:00
05. Linear Boundaries
06. Higher Dimensions00:00:00
06. Higher Dimensions
07. Perceptrons00:00:00
07. Perceptrons
08. Why Neural Networks00:00:00
09. Perceptrons as Logical Operators00:00:00
09. Perceptrons as Logical Operators Lab00:00:00
09.2 Perceptrons as Logical Operators00:00:00
09.2 Perceptrons as Logical Operators Lab
09.2 Perceptrons as Logical Operators Lab
09.3 Perceptrons as Logical Operators Lab00:00:00
09.4 Perceptrons as Logical Operators00:00:00
09.5 Perceptrons as Logical Operators
09.5 Perceptrons as Logical Operators
10. Perceptron Trick00:00:00
10. Perceptron Trick
10.2 Perceptron Trick00:00:00
10.3 Perceptron Trick00:00:00
10.3 Perceptron Trick
11. Perceptron Algorithm00:00:00
11. Perceptron Algorithm Lab00:00:00
12. Non-Linear Regions00:00:00
13. Error Functions00:00:00
14. Log-loss Error Function00:00:00
14. Log-loss Error Function
15. Discrete vs Continuous00:00:00
15.2 Discrete vs Continuous00:00:00
15.2 Discrete vs Continuous
16. Softmax00:00:00
16.2 Softmax00:00:00
16.2 Softmax
16.3 Softmax00:00:00
16.3 Softmax00:00:00
17. One-Hot Encoding00:00:00
18. Maximum Likelihood00:00:00
18.2 Maximum Likelihood00:00:00
18.2 Maximum Likelihood
19. Maximizing Probabilities00:00:00
19.2 Maximizing Probabilities00:00:00
19.2 Maximizing Probabilities
20. Cross-Entropy 100:00:00
21. Cross-Entropy 200:00:00
21.2 Cross-Entropy 200:00:00
21.2 Cross-Entropy 2 Lab00:00:00
22. Multi-Class Cross Entropy00:00:00
22. Multi-Class Cross Entropy
23. Logistic Regression00:00:00
23.2 Logistic Regression00:00:00
24. Gradient Descent00:00:00
24.2 Gradient Descent
24.3 Gradient Descent
25. Logistic Regression Algorithm00:00:00
26. Pre-Lab Gradient Descent
27. Notebook Gradient Descent
28. Perceptron vs Gradient Descent00:00:00
29. Continuous Perceptrons00:00:00
30. Non-linear Data00:00:00
31. Non-Linear Models00:00:00
32. Neural Network Architecture00:00:00
32.2 Neural Network Architecture00:00:00
32.3 Neural Network Architecture00:00:00
32.4 Neural Network Architecture00:00:00
33. Feedforward00:00:00
33.2 Feedforward00:00:00
32.2 Neural Network Architecture
34. Backpropagation00:00:00
34.2 Backpropagation00:00:00
34.3 Backpropagation00:00:00
34.4 Backpropagation00:00:00
35. Pre-Lab Analyzing Student Data
36. Notebook Analyzing Student Data
37. Outro
Implementing Gradient Descent
01. Mean Squared Error Function
02. Gradient Descent00:00:00
03. Gradient Descent The Math00:00:00
04. Gradient Descent The Code00:00:00
05. Implementing Gradient Descent00:00:00
06. Multilayer Perceptrons00:00:00
06. Multilayer Perceptrons Lab00:00:00
07. Backpropagation00:00:00
07. Backpropagation Lab00:00:00
08. Implementing Backpropagation
08. Implementing Backpropagation00:00:00
09. Further Reading
Training Neural Networks
01. Instructor
02. Training Optimization00:00:00
03. Testing00:00:00
04. Overfitting and Underfitting00:00:00
05. Early Stopping00:00:00
06. Regularization00:00:00
07. Regularization 200:00:00
08. Dropout00:00:00
09. Local Minima00:00:00
10. Random Restart00:00:00
11. Vanishing Gradient00:00:00
12. Other Activation Functions00:00:00
13. Batch vs Stochastic Gradient Descent00:00:00
14. Learning Rate Decay00:00:00
15. Momentum00:00:00
16. Error Functions Around the World00:00:00
06. Regularization
Deep Learning with PyTorch
01. Instructor
02. Introducing PyTorch
03. PyTorch Tensors00:00:00
04. Defining Networks00:00:00
05. Training Networks00:00:00
06. Fashion-MNIST Exercise00:00:00
07. Inference Validation00:00:00
08. Saving and Loading Trained Networks00:00:00
09. Loading Data Sets with Torchvision00:00:00
10. Transfer Learning00:00:00
11. Transfer Learning Solution
Create Your Own Image Classifier
01. Instructor
02. Project Intro00:00:00
03. Introduction to GPU Workspaces
04. Updating to PyTorch
05. Image Classifier – Part 1 – Development
06. Image Classifier – Part 1 – Workspace
07. Image Classifier – Part 2 – Command Line App
08. Image Classifier – Part 2 – Workspace
09. Rubric
Project Description – Create Your Own Image Classifier
Project Rubric – Create Your Own Image Classifier
How Do I Continue From Here
01. Next Steps into the AI World!00:00:00
Level
Intermediate
Lectures
453 lectures
Material Includes
- Workspaces
- Hands-on Projects
- Quizzes
- Progress Tracker
