Overview
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- Module 1: Data exploration and analysis is at the core of data science. Data scientists require skills in languages like Python to explore, visualize, and manipulate data.
- Common data exploration and analysis tasks.
- How to use Python packages like NumPy, Pandas, and Matplotlib to analyze data.
- Module 2: Regression is a commonly used kind of machine learning for predicting numeric values.
- When to use regression models.
- How to train and evaluate regression models using the Scikit-Learn framework.
- Module 3: Train and evaluate classification models
- When to use classification
- How to train and evaluate a classification model using the Scikit-Learn framework
- Module 4: Clustering is a kind of machine learning that is used to group similar items into clusters.
- When to use clustering
- How to train and evaluate a clustering model using the scikit-learn framework
- Module 5: Train and evaluate deep learning models
- Basic principles of deep learning
- How to train a deep neural network (DNN) using PyTorch or Tensorflow
- How to train a convolutional neural network (CNN) using PyTorch or Tensorflow
- How to use transfer learning to train a convolutional neural network (CNN) with PyTorch or Tensorflow
In this module, you will learn:
In this module, you'll learn:
In this module, you'll learn:
In this module, you'll learn:
In this module, you will learn:
