Git Product home page Git Product logo

artificial-intelligence-and-machine-learning-fundamentals's Introduction

GitHub issues GitHub forks GitHub stars PRs Welcome

Artificial Intelligence and Machine Learning Fundamentals

Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing you to Python and discussing using AI search algorithms. You will learn math-heavy topics, such as regression and classification, illustrated by Python examples.

You will then progress on to advanced AI techniques and concepts, and work on real-life data sets to form decision trees and clusters. You will be introduced to neural networks, which is a powerful tool benefiting from Moore's law applied on 21st-century computing power. By the end of this course, you will feel confident and look forward to building your own AI applications with your newly-acquired skills!

What you will learn

  • Understand the importance, principles, and fields of AI
  • Learn to implement basic artificial intelligence concepts with Python
  • Apply regression and classification concepts to real-world problems
  • Perform predictive analysis using decision trees and random forests
  • Perform clustering using the k-means and mean shift algorithms
  • Understand the fundamentals of deep learning via practical examples

Hardware requirements

For an optimal student experience, we recommend the following hardware configuration:

  • Processor: 2.6 GHz or higher, preferably multi-core
  • Memory: 4GB RAM
  • Hard disk: 35 GB or more
  • An Internet connection

Software requirements

You’ll also need the following software installed in advance:

  • Operating System: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X
  • Browser: Google Chrome, latest version
  • Anaconda, latest version
  • IPython latest version

artificial-intelligence-and-machine-learning-fundamentals's People

Contributors

steffimonteiro avatar vishalmewadapackt avatar vishalmewara avatar zsolt-nagy avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar

artificial-intelligence-and-machine-learning-fundamentals's Issues

Lesson 1 - Exercise 01 Introduction to numpy

I think you should rename the exercise file from numpy.py. This can cause errors when you run the code. Because since numpy.py exists in the directory where you'll be running the code, it will import that instead of the numpy module that we installed.

Lesson 2 - Exercise 02

line 6 defines a function def succ. I think since this if for successors the actual word can be used to name the function. There is a similar funciton name in Exercises 3,4 and 5

Also you can give two empty lines above the function for readability reasons.

Lesson 2 - Activity 05

Running gameloop() gives and error on lines 63 and 64.

This
row = int( input( prompt='Enter row: ' ) )

should look like this
row = int(input('Enter row: '))

Lesson 7 - Activity 16 & 17

NameError: name 'confusion_matrix' is not defined on line 80 of digits.py and 88 of digits_deep.py

hint: confusion_matrix was not imported

Lesson 3 - Exercise 09

  1. Running the following modules modules gives a NameError: name 'model_selection' is not defined on line 9
  • model_fitting_linear.py
  • model_fitting.py
  • model_fitting._fib.py
  1. Plots not showing after running the following. Adding plot.show() at the end solves this issue
  • model_fitting_best_fit_line.py
  • model_fitting_np_polyfit.py

Lesson 1 - Activity 2

Running gameloop() gives and error on lines 55 and 56.

This
row = int( input( prompt='Enter row: ' ) )

should look like this
row = int(input('Enter row: '))

The input function doesn't take keyword arguments

Also it's not pythonic to leave spaces in a bracket, example ( input( "Enter row: " ) ) should be like this (input("Enter row: "))

Lesson 6 - Activity 15

  1. Wrong file name used in lines 6, 24 and 37 of image_clustering.py
    hint: the file name starts with a capital letter

  2. KMeans model was not imported

  3. There seems to be a lot of repeated code in image_clustering.py. Can you try to use functions. Also you're loading and reading the same image inside two for loops. Can you take that process out of for loops and find a way to load it just once.

Add Comments to code files

It will be nice to add comments to all the code files so that readers of the code can understand what is going on. At the point there is none at all

Lesson 3 - Exercise 08

  1. running cross_validation.py gives a NameError: name 'preprocessing' is not defined error.

  2. I'm wondering why it says cross validation but we aren't doing any cross validation. Scikit-learn has a cross_val_score function for doing cross validation. I think it could be very helpful here.

Lesson 4 - Exercise 16

  1. Please check line 6, dataFrame.replace() gives a syntax error.
    I fixed it by copying the variable name dataFrame and replacing it with that of line 6

  2. Pandas is not imported. Causes it to throw and error.

Lesson 1 - Activity 1

Numpy has been imported as np.

On line 9, numpy.matrix.transpose(A) throws a NameError

should be np.matrix.transpose(A) instead.

Please hold on commits that change the folder Structure.

Hi @zsolt-nagy

Please I would like to ask if you could hold making commits while I'm reviewing the codes. I downloaded the code to my machine to review and run them so if you make commits it worries.

I see you changed the location of some activities and it's messed with the names of my issues and some of the comments. Thanks

Lesson 5 - Activity 13 & 14

  • You are using a function called classification_report which is not defined. It returns the following error.

NameError: name 'classification_report' is not defined

This happens on

  • line 44, cars.py Activity 13
  • line 48 cars.py Activity 14

Solution:

Import classifcation_report from sklearn.metrics lik so

from sklearn.metrics import classification_report

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.