Deep Learning with Python: CNN, ANN & RNN

EDUCBA via Coursera Specialization

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Overview

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This Specialization provides a practical, project-driven pathway to mastering deep learning with Python. Learners will explore Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Recurrent Neural Networks (RNNs) with LSTM layers through real-world case studies in image recognition, customer churn prediction, and stock price forecasting. Each course emphasizes both theory and hands-on coding using TensorFlow and Keras, ensuring you graduate with job-ready AI skills and the ability to apply neural networks to authentic business and financial problems.

Syllabus

Course 1: Master CNNs with Python: Build, Train & Evaluate Models
Course 2: Deep Learning with ANN in Python: Build & Optimize
Course 3: Deep Learning RNN & LSTM: Stock Price Prediction

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Discover how deep learning can be applied to stock price prediction by building a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) layers in Python. This hands-on course guides you through the complete workflow, from setting up your development environment and preparing financial datasets to training, evaluating, and visualising a deep learning model for time-series forecasting. You will learn how to analyse stock price data, perform exploratory data analysis, preprocess datasets, apply feature scaling and consistent data transformations, and construct an RNN that captures sequential patterns in financial data. Using real-world Apple stock price data, you will train an LSTM-based model, generate predictions on unseen data, and evaluate forecasting performance through visual comparison with actual stock prices. Designed for beginners in data science as well as learners who want to strengthen their deep learning and time-series forecasting skills, this course emphasises practical implementation rather than isolated concepts. By connecting data preparation, neural network development, prediction, and performance evaluation into a single project, you will gain the confidence to build and assess RNN models for stock price forecasting using real-world financial data.
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Master the fundamentals of Deep Learning by building and optimising Artificial Neural Networks (ANNs) in Python through a structured, hands-on learning experience. This course guides you from configuring a Python environment with Anaconda and TensorFlow to preprocessing and encoding data, constructing ANN architectures, generating predictions, and improving model performance with resampling techniques for imbalanced datasets. Designed for students, data enthusiasts, and professionals looking to strengthen their deep learning skills, the course combines practical implementation with clear explanations to help you understand every stage of the ANN workflow. You will learn how to prepare data for training, build neural network models using TensorFlow and Keras, apply activation functions, evaluate predictions, and optimise model performance using industry-standard practices. A distinguishing feature of this course is its end-to-end, project-based approach. Rather than focusing on isolated concepts, it connects environment setup, data preparation, model development, and evaluation into a complete workflow using a customer churn analysis scenario. Through guided lessons, practical exercises, and quizzes, you will gain the confidence to build, evaluate, and optimise ANN models in Python while developing a strong foundation for further study in deep learning.
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Master the foundations of Convolutional Neural Networks (CNNs) and learn how to apply, build, and evaluate deep learning models using Python. This course provides a structured, hands-on introduction to CNNs, guiding you from project setup and core CNN concepts to implementing models, preprocessing and augmenting image datasets, generating predictions, and evaluating model performance. Through practical coding activities and assessments, you will strengthen both your conceptual understanding and your ability to develop CNN-based image classification solutions. Designed for beginners and learners transitioning into deep learning, this course combines clear explanations with applied Python implementation to help you build confidence in computer vision workflows. You will learn how CNN architectures work, apply preprocessing techniques to prepare image data, compare model accuracy, and evaluate performance to understand how architectural choices influence results. Its practical, modular structure reinforces every concept through hands-on learning and graded quizzes, ensuring that theory is consistently connected to real implementation. By the end of the course, you will be able to design, implement, test, and evaluate CNN models for image classification tasks using Python, building a strong foundation for further study and practical deep learning applications.