Artificial Intelligence Bootcamp

Introduction to Python

This introduction to Python course covers essential programming concepts for data science. You’ll learn scripting basics, algorithms, and data structures like tuples and dictionaries, and explore Python libraries and functions. The course ends with a project where you’ll develop a Python script to analyze data, preparing you to tackle real-world data science challenges.

What you’ll learn:

• Apply the basics of programming language methodologies to real world scenarios
• Demonstrate foundational skills for scripting with a programming language, python

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Cloud Computing, Generative AI, and Dashboards

This course focuses on cloud computing for cost-effective, scalable data processing. You’ll master technical components like PySpark to integrate Python, SQL, and Spark for handling structured and semi-structured data. Using libraries such as Numpy, Pandas, and PySpark, you’ll work with big data and create stunning visualizations with Python libraries like Seaborn. The course also explores advanced data analysis using generative AI and interactive dashboards, culminating in a project that brings big data to life through visualizations.

What you’ll learn:

• Create a dashboard using data science methodologies with industry standard tool(s)
• Model exploratory data analysis with tools for multiple data sets. (SQL and SQL table relations)
• Utilize programming techniques to process large data samples with (PySpark and Big Data)

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Introduction to Machine Learning

This course introduces the fundamentals of AI and machine learning, covering core concepts like statistical learning theory and supervised learning. You’ll explore models such as logistic regression, decision trees, and support vector machines, and learn to evaluate them using metrics like ROC AUCs. The course concludes with a project where you’ll select and deploy the ideal model for a specific task, demonstrating your mastery of the data science pipeline.

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

Machine Learning with Scikit-Learn

This course covers both supervised and unsupervised machine learning models. You’ll learn about distance metrics and k-Nearest Neighbors for classification, recommender systems using SVD, clustering techniques like k-means, and dimensionality reduction with PCA. The course concludes with a project where you’ll build and demonstrate both a supervised (k-Nearest Neighbors) and an unsupervised (k-means) learning model, showcasing your skills in 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

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Large Language Models

This course covers large language models (LLMs) and their practical applications. You’ll learn to extract insights from text, time, and image data using neural networks and natural language processing (NLP). The course also integrates key concepts from mathematics, statistics, and probability to enhance your data and AI skills.

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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Natural Language Processing, Time Series and Neural Networks

This course teaches skills to build advanced models, focusing on natural language processing (NLP) with techniques like text classification and vectorization, time series analysis for managing and visualizing trends, and neural networks using Keras. The course culminates in a project where you’ll build and showcase three 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 and Similar Models

This course builds on neural network fundamentals, teaching optimization techniques like normalization and regularization. You’ll explore Convolutional Neural Networks (CNNs) for image classification, Recurrent Neural Networks (RNNs) for forecasting and sequence data, and advanced models like transformers and BERT. The course concludes with a project where you’ll demonstrate your expertise by 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

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Artificial Intelligence Capstone

In this intensive capstone you will be tasked with developing 3 different portfolio projects and you will be expected to frame your projects around solving a business problem. You will bring together all of your skills from foundations together to build 3 different methods; a regression model, a classification supervised mode, and a classified unsupervised model.

What you’ll do:

• Integrate data science process using at least one method of regression
• Integrate data science process using at least one method of non-regression supervised learning
• Integrate data science process at least one method of non-regression unsupervised learning

Once you’ve completed the full bootcamp program – essentials through capstone – you’ll receive a completion certificate and be ready to jump head-first into the job search with the help of your dedicated Flatiron School career coach.