Introduction to Deep Learning

For IT leaders: When junior data scientists join, many spend their first months figuring out how to move from classic machine learning to deep learning for images, text, and time-series data. This course gives your team a shared TensorFlow-based workflow for that work, from convolutional neural networks and transfer learning to NLP sequence models and forecasting.

• Role fit: Associate-level data scientists who already program in Python, use libraries like pandas and scikit-learn, and understand supervised and unsupervised learning. The course directly follows Introduction to Machine Learning. Assign that course first to anyone whose fundamentals need a refresh.
• Time investment: 38 skills totaling about 34 hours. Most skills run 45–70 minutes, so one or two per week fits alongside project work.
• How learners work: Learners type code alongside the trainer in Google Colab or Jupyter notebooks. The trainer works through setup errors and notebook debugging live. Most skills close with a challenge and a solution video, including Kaggle-style projects such as disaster tweet classification.
• Rollout note: Large-dataset fine-tuning and NLP training run better on a GPU. The course covers both Colab GPU runtimes and local environments, so confirm which your organization allows before you assign it.

Use it to onboard new data scientists consistently, or keep it as a team reference when a project calls for CNNs, LSTMs, or pre-trained TensorFlow Hub models.

Data science teams often stall between a promising proof of concept and a model that holds up on messy, real-world data. This course closes that gap through hands-on projects built in TensorFlow, so new data scientists reach productive work faster and Team Leads spend less time re-teaching fundamentals.

The course scenarios mirror real project work. Learners start with a Teachable Machine proof of concept for a restaurant use case, then rebuild it as a real CNN. They use image augmentation such as rotation, shifting, and zoom to handle cluttered real-world photos. They also explore natural language processing and text classification concepts, then build a sunspot forecast model with DNN, LSTM, and CNN architectures.

• Faster time to a working baseline: Practitioners learn to build a baseline model first, then improve on it step by step. Teams get repeatable progress instead of trial-and-error experiments.
• Fewer models that fail in production: Visualization techniques help learners spot overfitting early, before a model that looks good in training disappoints on new data.
• Less training from scratch: Transfer learning, callbacks, and fine-tuning with TensorFlow Hub and Keras applications models let teams reuse pre-trained architectures such as ResNet and EfficientNet.
• Better architecture decisions: Learners practice matching RNN, LSTM, GRU, and bidirectional models to the use case. They write down the expected effects on accuracy and real-world feasibility, so model choices are easier for others to review.

The course also prepares practitioners for everyday friction, including notebook execution-order bugs, environment setup errors, and limited GPU availability in Colab. Fewer hours are lost to setup problems that can derail a junior hire's first projects.

Knowledge

• How deep learning differs from classical machine learning, and where neural networks fit in computer vision, NLP, and time-series forecasting
• How convolutional neural networks are built, including convolutional, ReLU, pooling, flatten, and softmax layers, and how they compare to deep and shallow baseline networks
• How overfitting appears in training results, and how visualization, data augmentation, and callbacks help detect and control it
• When to use transfer learning, feature extraction, or fine-tuning with pre-trained models
• How tokenization, vocabularies, and embeddings let machines read text, and when an RNN or LSTM fits a given sequence problem
• How the patterns in time-series data shape the choice of DNN, RNN, LSTM, or CNN forecasting architectures

Ability

• Set up a working TensorFlow environment in Google Colab or local Jupyter notebooks, and work through common notebook, path, and GPU issues
• Ingest and augment real-world image data, then build, evaluate, and improve binary and multi-class CNN classifiers
• Reuse and fine-tune pre-trained models on reduced and full-size datasets using the TensorFlow Data API
• Build sentiment and text classifiers on real datasets, such as Yelp reviews and Kaggle disaster tweets, and prepare a submission for a Kaggle contest
• Generate text with recurrent neural networks
• Build and compare DNN, LSTM, and CNN forecasting models on sunspot time-series data