Building a Machine Learning (Artificial Neural Network) Model - Python Data Science Intro Project

Building a Machine Learning (Artificial Neural Network) Model - Python Data Science Intro Project


Full Code From This Example:

YouTube Description:

We get pretty involved in this one - let me know if you like this style of full builds okay or prefer the quick five minute pieces! In this one we'll look at how we can create a machine learning model, an artificial neural network, ANN, to do classification predictions on a data set. We'll be using pandas and numpy for processing the data, tensorflow to build our model, and matplotlib to visualize data. Machine Learning -- Machine Learning sounds scary, but in reality it's just allowing our computer to try to find patterns in data without really giving it instructions on how to do it. We "teach" these patterns by giving a model training data and training labels (the outputs we expect.) By knowing the "answers" and the features, a model can reverse engineer the patterns. We can then use this trained model to predict data the model hasn't seen. Artificial Neural Network -- Artificial Neural Network, ANN, is a type of machine learning that uses nodes and tries to resemble the human brain. We teach these models by constructing nodes together into layers. We then construct multiple layers together into a model. A node receives a summation of inputs from all previous nodes firing to it. If the input is high enough, this will trigger the node to "fire" itself. What the node "fires" is a product of the summation of the input and the activation function. If the threshold is not met, the node may not fire. The first layer is the input layer, the last layer is the output layer, and all the layers in between are "hidden" layers. They are called hidden because we are unable to see both their inputs and outputs. It is a mystery as to what is happening in these layers. "Deep Learning" is a term used to coin these hidden layer interactions. The more hidden layers, the higher the level of complexities our model can learn. More layers doesn't always necessarily mean better results, as we can "overfit" data. over fitting -- Over fitting is where we train our model and the model thinks it has "learned" patterns that are always true but really aren't. The patterns just exist in the training data but aren't representative of the entire population. We can check overfitting by using validation data in our machine learning. Data Science -- A little more complex than what I could put here, but data science is mainly focused on how we can interpret data into the future. Building models from previous data to explore and predict future events, classifications, etc. Thanks so much for watching the video! It's incredible how far the channel has come. My style in the beginning videos was to be as quick and straight to the point as possible. My mentality was lower video times, more people are likely to click. Thanks to everyone supporting me along the way, I feel like I can put a little more personality into videos without the worry of it only ever being viewed by me. 5867 subscribers at the time of writing, how dope. If you've watched any of my videos, subscribed to the channel, or supported me in any other way, thank you so much. You're incredible. Join The Socials -- Picking Shoutouts Across YouTube, Insta, FB, and Twitter! FB - Insta - Twitter - LinkedIn - GitHub - ***************************************************************** Full code from the video: Packages (& Versions) used in this video: Python 3.7 TensorFlow 2.0 NumPy 1.17 Pandas Matplotlib sklearn Atom Text Editor ***************************************************************** Code from this tutorial and all my others can be found on my GitHub: Check out my website: If you liked the video - please hit the like button. It means more than you know. Thanks for watching and thank you for all your support!! Always looking for suggestions on what video to make next -- leave me a comment with your project! Happy Coding!

Enjoy this content? Consider Subbing to the Youtube channel

Derrick Sherrill

By: Derrick Sherrill

Thanks for visiting my page! I'm working hard to make the best content I can for you. I love watching people learn and teaching others. Happy Coding!

Become a Patreon!