Path overview
On this path, you’ll learn all about deep learning, including how to build, train, and evaluate models with the TensorFlow framework.
You’ll then learn how to conduct forecasts on real data by applying sequential neural network models to time series forecasting.
Next, you’ll learn how to use TensorFlow tools and libraries to work on a range of NLP use cases, including text visualization, sentiment analysis models, and more.
Finally, you’ll learn how to apply convolutional neural networks (CNNs) to computer vision tasks so that you can teach computers to see and interpret digital images.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. You’ll apply your skills to several guided projects with realistic business scenarios to build your portfolio and prepare for your next interview.
Key skills
- Building and evaluating deep learning regression models using TensorFlow’s Sequential and Functional APIs in Keras
- Implementing a time series forecast model using LSTM and GRU
- Building and evaluating a deep learning sentiment classification model using Transformers.
- Building and training a CNN for image classification tasks
Path outline
Part 1: Deep Learning in TensorFlow [4 courses]
Introduction to Deep Learning in TensorFlow 5h
Objectives- Explain the major concepts and terminology used in deep learning
- Perform data preprocessing and exploratory analysis to prepare data for modeling
- Build and evaluate deep learning regression models using TensorFlow’s Sequential and Functional APIs in Keras
- Build a deep neural network to predict listing gains of IPOs on the Indian market
Sequence Models for Deep Learning 12h
Objectives- Describe sequential neural network models
- Determine when to use RNN, GRU, and LSTM
- Implement a sequential model using a basic RNN
- Implement a time series forecast model using LSTM and GRU
Natural Language Processing for Deep Learning 12h
Objectives- Process and explore text data
- Visualize text data using a word cloud
- Implement tokenization and word embeddings
- Build sequence models
- Build a transformer-based text classification model
Convolutional Neural Networks for Deep Learning 12h
Objectives- Explain the basics of convolutional neural networks (CNNs)
- Build and train a CNN for an image classification task
- Implement regularization techniques
- Fine-tune a CNN model using transfer learning
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Projects in this path
Predicting Listing Gains in the Indian IPO Market Using TensorFlow
For this project, you’ll work as a data scientist for an investment firm analyzing the Indian IPO market. You’ll build a deep learning model using TensorFlow to predict listing gains, applying skills in data exploration, visualization, preprocessing and modeling.
Time-Series Forecasting on the S&P 500
For this project, you’ll take on the role of a trader on the S&P 500 futures desk aiming to build a model to forecast the index’s movement, enabling lucrative trading opportunities.
Classifying Disaster-Related Tweets as Real or Fake
For this project, you’ll be a data scientist at a news company building a deep learning model to predict if tweets are about real disasters or not. Using a Kaggle dataset, you’ll apply Python, TensorFlow, and NLP to gain in-demand skills.
Detect Pneumonia Using X-Ray Images with CNNs and Transfer Learning
For this project, you’ll assume the role of a Deep Learning Engineer tasked with developing models to help hospitals diagnose pneumonia in children using chest X-ray images.
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