Introduction to Deep Learning Online Training

Organizations adding deep learning to analytics or application workflows need more than experimentation; they need junior data scientists who can build, compare, and improve models consistently. This course fits teams with Python and basic machine learning experience who are moving into TensorFlow-based computer vision, NLP, and time-series forecasting. Plan for about 34 hours per learner, making it best suited for structured onboarding, a team upskilling sprint, or a shared reference path for data science practitioners. IT Directors and Training Managers should expect change-management value from the course’s progression: learners move from foundational concepts and environment setup into applied model building, performance improvement, transfer learning, sentiment analysis, and forecasting. CBT Nuggets Playlists can help assign this as a cohort path, while Team Reporting helps leaders track learner progress and completion across the team.

IT teams complete this training to move junior data scientists from theory into repeatable TensorFlow workflows across common business data types: images, text, and time series.

• Build and improve image classifiers, including dog-or-cat classification, multi-class CNNs, and models that ingest real-world image data.
• Reduce model development time by applying transfer learning with TensorFlow Hub, Kaggle models, ResNet, and EfficientNet comparisons.
• Support text-driven business decisions by building sentiment analysis workflows with embeddings, vector spaces, Yelp data, and disaster tweet classification.
• Expand forecasting capability by comparing DNN, RNN, LSTM, and CNN approaches for time-series patterns such as sunspot forecasting.

Knowledge

• Deep learning fundamentals and how they differ from traditional machine learning approaches.
• TensorFlow development environment setup for deep learning projects.
• Core computer vision concepts, including CNN architecture, overfitting, callbacks, and transfer learning.
• NLP concepts such as tokenization, sequences, embeddings, sentiment analysis, RNNs, LSTMs, and GRUs.
• Time-series concepts, temporal patterns, and model choices for forecasting.

Ability

• Build TensorFlow CNN models for binary and multi-class image classification.
• Ingest real-world image data and improve CNN performance through visualization and tuning.
• Reuse and fine-tune pre-trained TensorFlow Hub models on smaller and larger datasets.
• Create TensorFlow NLP classifiers for sentiment analysis and disaster tweet classification.
• Compare DNN, RNN, LSTM, CNN, GRU, and bidirectional LSTM approaches for practical modeling tasks.