Tools: Python, Tensorflow, NumPy, Pandas, Matplotlib, Seaborn, Plotly, Cufflinks
Prerequisite: Basic Programming, Familiarity with Tensorflow, NumPy, Pandas, Matplotlib
Students will take home: Project files
Location: 1073 S De Anza Blvd, San Jose, CA 95129
- Recap of the following topics:
- Linear Regression
- Logistic Regression,
- K Nearest Neighbors
- Decision Trees and Random Forests
- Support Vector Machines
- K Means Clustering
- Principal Component Analysis
- Natural Language Processing
- Neural Networks and Deep Learning
Investigate one of the E-commerce biggest problems and come up with a model to perform Credit Card Fraud Detection.
Develop a model to predict the quality of loan applications based on their financial history and convert the promising leads to sales.
Improve customer retention by developing a model that minimizes churn of subscription product through analysis of financial habits.
Inspired by Amazon Echo Look Style Assistant, we are going to develop a model to identify and classify fashion images.
Develop a model that analyzes the app behavior and directs the customers to subscription products.
We will also learn some new algorithms along the way including Polynomial Regression, Support Vector Regression, Decision Tree Regression, Random Forest Regression, Linear Discriminant Analysis, XGBoost and Convolutional Neural Networks.
We will then pick one or two problems from Kaggle – an AirBnB for Data Scientists. This is where they spend their nights and weekends. It’s a crowd-sourced platform to attract, nurture, train and challenge data scientists from all around the world to solve data science, machine learning and predictive analytics problems.