Certificate in Data Science

Objectives

This full-time course will provide everything you need to kickstart your career as a Machine Learning engineer.

About this course

This course will cover all of the following topics over a 3 month, full-time intensive course:

• Deep Learning / Neural Networks (MLP's, CNN's, RNN's)
• Regression analysis
• K-Means Clustering
• Principal Component Analysis
• Train/Test and cross validation
• Bayesian Methods
• Decision Trees and Random Forests
• Multivariate Regression
• Multi-Level Models
• Support Vector Machines
• Reinforcement Learning
• Collaborative Filtering
• K-Nearest Neighbor
• Bias/Variance Tradeoff
• Ensemble Learning
• Term Frequency / Inverse Document Frequency
• Experimental Design and A/B Tests
• Develop using iPython notebooks
• Understand statistical measures such as standard deviation
• Visualize data distributions, probability mass functions, and probability density functions
• Visualize data with matplotlib
• Use covariance and correlation metrics
• Apply conditional probability for finding correlated features
• Use Bayes' Theorem to identify false positives
• Make predictions using linear regression, polynomial regression, and multivariate regression • Understand complex multi-level models
• Use train/test and K-Fold cross validation to choose the right model
• Build a spam classifier using Naive Bayes
• Use decision trees to predict hiring decisions
• Cluster data using K-Means clustering and Support Vector Machines (SVM)
• Build a movie recommender system using item-based and user-based collaborative filtering
• Predict classifications using K-Nearest-Neighbor (KNN)
• Apply dimensionality reduction with Principal Component Analysis (PCA) to classify flowers
• Understand reinforcement learning - and how to build a Pac-Man bot

Prerequisites

Must have a working knowledge of Python