Predict the stock market with data and model building!

Learn the basics of TensorFlow in Python, set up a new project, explore computational graphs, train a simple linear regression model, and prepare for future machine learning tutorials.

Learn how placeholder nodes hold input values until a session runs, providing input for models like linear regression, and why every placeholder must receive a value before execution.

Explore how to build a linear regression model from scratch using computational graphs, placeholders, variables, and loss minimization to predict stock data.

Build and train a linear regression model in TensorFlow using placeholders, variables, and a gradient-descent optimizer to minimize loss.

Develop a simple stock market prediction model using daily volume to forecast next-day price movement for day trading, with a Python project and data from investing dot com.

Predict whether the next morning's stock price rises or falls using end-of-day volume, via a simple model trained on gold exchange stock data.

Import stock data from CSFB CSV sheets using pandas, clean values, and extract open prices and volumes. Create a load_stock_data function to return floating arrays for training and testing.

Explore the foundations of machine learning, neural networks, and convolutional neural networks through a no-coding, video-led session with text notes, focusing on four steps in building ML programs.

Explore neural networks, their layers, weights, biases, and activation functions that route input to output, and how training adjusts weights to recognize patterns for image classification.

Explore convolutional neural networks, which apply convolution and max pooling to image data, reduce input size, and highlight important features for better recognition.

Explore Keras and TensorFlow libraries to build different parts of a machine learning model, train, evaluate, and predict, while installing Keras and reviewing activation functions.

Learn how 60k train and 10k test images are stored as 32 by 32 rgb arrays with matching labels, then normalize and convert labels to categorical format for model training.

Learn to save and load trained models with H5 files, evaluate and use model predict functions, and freeze the graph for mobile import.

Build an image classifier using Karris and Python in a seven-day boot camp. Learn neural networks, convolutional networks, data preparation, and how to build, train, and save models for deployment.