Deep Learning with Python: CNN, ANN & RNN

By the end of this course, learners will be able to identify the foundations of deep learning, analyze stock price datasets, apply preprocessing and feature scaling techniques, develop an RNN with LSTM layers, and evaluate predictions using real-world financial data. This hands-on course takes learners through the complete journey of building a stock price forecasting model with Python. Starting with environment setup and dataset exploration, participants will learn how to preprocess data, perform exploratory data analysis, and apply transformations that prepare inputs for deep learning models. The course then dives into constructing and training a Recurrent Neural Network, leveraging LSTM layers to capture sequential dependencies in stock prices. Learners will test predictions on unseen data and visualize results to interpret model accuracy. What makes this course unique is its practical project-based approach—instead of abstract theory, every step is tied to real-world stock price data from Apple. Whether you are a data science beginner or looking to specialize in time-series forecasting, this course equips you with skills to confidently apply deep learning models to financial predictions and beyond.

By the end of this course, learners will be able to configure a Python environment, preprocess and encode data, build Artificial Neural Network (ANN) architectures, generate predictions, and address imbalanced datasets using resampling techniques. Participants will gain hands-on experience with TensorFlow, Keras, and Anaconda while mastering practical skills in data preparation, model construction, and performance optimization. This course benefits students, data enthusiasts, and professionals seeking to strengthen their deep learning expertise with a focused, project-based approach. Unlike generic tutorials, it emphasizes a complete end-to-end workflow—from environment setup and data preprocessing to ANN design and evaluation—ensuring learners can independently create predictive models. What makes this course unique is its balance between conceptual clarity and real-world implementation. Learners not only understand the theory but also apply it directly to customer churn analysis, a practical business use case. With step-by-step lessons, quizzes, and guided projects, this course equips participants with the confidence to implement ANN models in real scenarios and transition smoothly into more advanced deep learning topics.