AI & Machine Learning Foundations II

Curriculum

AI and Data Science Foundations II

Introduction to Machine Learning

FT: 1 week | PT: 3 weeks

In this course you will begin to learn the fundamentals of AI, machine learning models. Explore core concepts like statistical learning theory and supervised learning. Delve into diverse models like logistic regression, decision trees, and support vector machines. Learn to evaluate and compare their performance using metrics like ROC AUCs. Finally, in the culminating project, showcase your mastery of the data science pipeline by selecting and deploying the ideal model for a specific task.

What you'll learn:

• Utilize foundational machine learning modeling like decision trees and supervised learning
• Prepare data for machine learning modeling with preprocessing (feature extraction) and normalization
• Utilize mathematics, statistics, and probability for data science methodologies to derive insights

Course focus:

• AI and Machine Learning
• Modeling with data
• Logistical Regression
• Deploying a model

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Machine Learning with Scikit-Learn

FT: 1 week | PT: 3 weeks

In this course you will be introduced to a range of supervised and unsupervised machine learning models. You will explore distance metrics and the foundation for k-Nearest Neighbors, a popular supervised learning model for classification. Dive into recommender systems, leveraging SVD for both supervised and unsupervised learning tasks. Learn clustering techniques like k-means, and explore dimensionality reduction with Principal Component Analysis (PCA) for an unsupervised learning model. Finally, conquer the culminating project: build both a supervised (k-Nearest Neighbors) and unsupervised (k-means) learning model, showcasing your ability to tackle classification and clustering tasks.

What you'll learn:

• Utilize foundational machine learning modeling like decision trees and supervised learning
• Prepare data for machine learning modeling with preprocessing (feature extraction) and normalization
• Integrate mathematics, statistics, and probability for data science methodologies to derive insights

Course focus:

• Supervised and unsupervised machine learning
• Principal Component Analysis (PCA)
• Deploying models

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Natural Language Processing, Time Series & Neural Networks

FT: 1 week | PT: 3 weeks

This course equips you with the skills to build cutting-edge models. Master natural language processing (NLP), exploring techniques like text classification, and vectorization. Delve into time series analysis, learning to manage, visualize, and model trends in data. Finally, dive into the fascinating world of neural networks, understanding their theory and implementation with Keras. In the culminating project, showcase your mastery by building three distinct models: a language model, a time series model, and a basic neural network.

What you'll learn:

• Develop insights from language, time, and image data using neural networks and Natural Language Processing (NLP)
• Integrate mathematics, statistics, and probability for data science methodologies to derive insights

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Neural Networks & Similar Models

FT: 1 week | PT: 3 weeks

In this course you will learn how to build upon your neural network foundation. Master normalization and regularization techniques to optimize your models. Delve into Convolutional Neural Networks (CNNs) for powerful image classification. Explore Recurrent Neural Networks (RNNs) and unlock their potential for forecasting and sequence data analysis. Finally, unveil the cutting-edge world of transformers and BERT, culminating in a project that showcases your expertise in building an advanced neural network application.

What you'll learn:

• Create an advanced neural network application
• Integrate mathematics, statistics, and probability for data science methodologies to derive insights

Course focus:

• Advanced Neural Network
• Advanced Neural Network Application

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