Detect Fraud and Predict the Stock Market with TensorFlow

Set up a Python development environment by downloading and installing Python 3.6.2 and PyCharm community edition, configuring the interpreter, and creating a new project to run Python programs.

Install and configure PyCharm, set a Python interpreter (e.g., Python 3.6.2), create new Python files, and run programs using run configurations and the console.

Explore Python variables and basic types, including numbers and strings, as you learn single value variables, operations, printing, and comments in a dynamic, weakly typed language.

Master multivalue variables in Python by using tuples, lists (arrays), and dictionaries. Learn to implement lengths, max/min, indexing, slicing, appending, inserting, removing, and multi-dimensional lists.

Master control flow with if, else if, and else blocks, plus while and for loops, testing conditions and nesting logic via traffic light examples.

Explore how classes and objects model real-world entities in Python, define properties and behaviors, create instances with initializers, and apply inheritance and polymorphism with methods.

Begin with an introduction to TensorFlow in Python, set up a project, explore core components and computational graphs, and train a simple linear regression model.

Uncover how TensorFlow uses tensors and a computation graph to build, train, and run models, from data preparation and linear regression basics to training versus testing and learning rates.

Learn to build a TensorFlow computational graph using constant and operation nodes, run sessions to evaluate values, and explore how to connect nodes for a basic linear regression model.

Learn how placeholder nodes hold no value until session run, then receive input data to drive model computations in TensorFlow.

Explore variable nodes and their differences from constant nodes, including initialization via a global variables initializer. Learn how to assign, reassign, and run operations on variables within TensorFlow.

Explore how to build a regression model using linear regression concepts with TensorFlow, including placeholders, variables, loss minimization, and training to predict outputs from inputs.

Build a linear regression model in TensorFlow by constructing a computational graph with variables and placeholders, train it via gradient descent to minimize loss, and test its accuracy.

Explore building a basic credit card fraud detection model with TensorFlow, from dataset preprocessing and train-test split to training and evaluating a linear regression model's accuracy.

Introduce a large credit card transactions dataset, address class imbalance with an IPCA transformation, and prepare 28 features for training and testing fraud detection via X_train, X_test, y_train, and y_test.

Split the fraud dataset into four sets—X_train, y_train, X_test, y_test—shuffle to reduce bias, then one-hot encode labels, normalize features to 0–1, and convert to numpy arrays for TensorFlow.

Apply logic racing to balance the training data by weighting fraudulent transactions more heavily, reducing dataset bias and improving fraud detection.

Build a three-layer neural network within a TensorFlow computational graph to predict fraud, connecting training and testing inputs, applying dropout, sigmoid, and softmax for cross-entropy loss optimization.

Train the model by building a TensorFlow computation graph, initializing variables, and running a training loop that minimizes cross-entropy with an Adam optimizer, feeding in X_train and y_train.

Learn how to test and evaluate a fraud detection model built with TensorFlow, monitor accuracy and loss during training, and interpret overall versus fraud-specific performance.

Build a simple stock market prediction model with TensorFlow that forecasts next-day price movement from daily volume for day trading, using Python and CSP sheets for four stocks from Investing.com.

Build a TensorFlow model to predict whether a stock price will rise or fall the next day for day trading based on end-of-day volume, starting with data exploration.

Import and format data by loading CSP files, extracting the price open and volume columns, cleaning numeric strings (removing commas), converting to floats, and returning arrays for model input.

Calculate price differences by subtracting the current day's final price from the next day's opening price, creating a differences list for training and testing a TensorFlow stock model.

learn to build a simple TensorFlow computational graph for linear regression y = w x + b, with placeholders, variables, squared-loss, and atom optimizer training.

Create and train a TensorFlow session, initialize variables, and configure epochs to train and test a stock market model using price differences, placeholders, and data loading.

train a stock-prediction model, optimize weights w and b to minimize loss, and test accuracy using a custom calculation against volumes and price differences.

Learn to extend a TensorFlow stock prediction model by importing multiple stock data sheets, creating per-stock variables, and testing across gold, gas, oil, and silver.