Description
Data Science: Deep Learning in Python – Real Projects, Real Code
This course cuts through the hype and teaches you **applied deep learning**—not just theory, but how to build, train, and deploy models that solve real problems. You’ll implement neural networks from scratch in NumPy, then scale up with TensorFlow and Keras to tackle image recognition, time series, and NLP.
Projects You’ll Build
- A facial expression recognizer using CNNs and FER2013 dataset
- A stock price predictor using RNNs and LSTMs
- A simple chatbot with sequence-to-sequence modeling
- A handwritten digit classifier from first principles
Key Concepts Covered
- Neural network fundamentals—backpropagation, activation functions, weight initialization
- Convolutional Neural Networks (CNNs)—for image and video data
- Recurrent Neural Networks (RNNs)—for sequences and time series
- Optimization techniques—momentum, RMSprop, Adam
- Regularization—dropout, batch normalization, early stopping
Why This Stands Out
Unlike toy tutorials, this course uses **real datasets and debugging workflows**—you’ll learn to handle vanishing gradients, overfitting, and slow convergence like a pro.
Who Should Enroll?
- Python developers entering deep learning
- Data scientists moving beyond scikit-learn
- Students needing portfolio projects
- Self-taught coders seeking structured knowledge
From Zero to Deployable Models
You’ll leave with a toolkit of reusable deep learning components—and the confidence to apply them to your own projects.
Stop watching—start building. Enroll today.
