1. Use Python and SQL to access and analyze data from several different data sources.
2. Build predictive models using a variety of unsupervised and supervised machine learning techniques.
3. Perform feature engineering to improve the performance of machine learning models.
4. Optimize, tune, and improve algorithms according to specific metrics like accuracy and speed.
5. Compare the performances of learned models using suitable metrics.
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Learners shall have to prove their skills by completing the following projects:
- Finding Donors for CharityML: Apply supervised learning techniques on data collected for the US census to help CharityML (a fictitious charity organization) identify groups of people that are most likely to donate to their cause.
- Create Your Own Image Classifier: Define and train a neural network in TensorFlow that learns to classify images; going from image data exploration to network training and evaluation.
- Identify Customer Segments with Arvato: Study a real dataset of customers for a company, and apply several unsupervised learning techniques in order to segment customers into similar groups and extract information that may be used for marketing or product improvement.
- Learners will be awarded Certificate of Completion co-issued by TP and Udacity in Artificial Intelligence Programming with Python upon completion of at least ONE project.
- Learners will be awarded the Nanodegree conferred by Udacity in Artificial Intelligence Programming with Python upon completion of ALL projects.