AI & Machine Learning Bootcamp

Introduction to Python

This introduction to Python course is designed to equip you with essential skills applicable to data science. Throughout this course, you’ll delve into fundamental programming concepts starting with scripting basics, understanding compiled vs. interpreted languages, and creating algorithms for simple tasks. You’ll explore operators, loops (while and for), and data structures like tuples, lists, dictionaries, and strings. Additionally, you’ll learn about libraries and functions, enabling you to leverage Python’s extensive ecosystem for complex tasks. This course culminates in a project where you are tasked with developing a Python script to analyze data in a file. By the end of the course, you will have developed your understanding to develop efficient code, and tackle real-world challenges in the technical domain of data science.

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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Introduction to Data Science

You will embark on an immersive journey into the world of data analysis and visualization using Python. Throughout this course, you’ll learn essential statistical measures, explore different types of data, and master data analysis techniques using the pandas library. You’ll delve into data visualization with Python libraries such as Seaborn and Matplotlib, gaining insights into qualitative, quantitative, and multivariate data. The course culminates in a project, wherein you will apply your knowledge to perform a full exploratory data analysis process, demonstrate proficiency in descriptive data analysis and visualization using pandas. By the end of this course, you’ll emerge equipped with the skills to gather insights from data, perform advanced data analysis, and effectively communicate your findings through visualizations and descriptive statistics.

What you’ll learn:

• Implement foundational statistical measurement with data using scripting
• Demonstrate gathering insights from data with visualizations
• Integrate object oriented programming (OOP) with Python for data cleaning and analysis

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Introduction to SQL

This course is designed to equip you with essential skills in structured query language (SQL), data engineering, database administration, and data analysis. Learn the essentials of mathematics, probability, and statistics for data science as well as learn how to perform more advanced data analysis and cleaning with Python. Throughout this course, you’ll start by getting familiar with SQL, learning how to connect to databases, and performing basic queries. As you progress, you’ll delve into more advanced topics such as filtering, ordering, and grouping data, as well as understanding table relations and implementing joins and subqueries. In the culminating project, you will demonstrate proficiency in SQL by applying queries on a database within a Python environment and reflecting on the database design and outputs. This project serves as a practical assessment of your ability to conceptually apply SQL knowledge and understanding of database concepts, preparing you for real-world data science challenges.

What you’ll learn:

• Utilize industry standard techniques to analyze data with programming language (Python), structured query language (SQL), and the cloud
• Explore and manipulate data with mathematics, probability, and statistics
• Analyze data for a business problem with visualizations with a dashboard

Cloud Computing, Generative AI, & Dashboards

This course dives into cloud computing’s cost-effective, scalable ecosystem for distributed data processing. Master technical components like PySpark to bridge Python, SQL, and Spark, to manipulate structured and semi-structured data. Leverage libraries to Numpy, Pandas, and PySpark to pull in “big data”. You will craft stunning visualizations with Python libraries like Seaborn. Finally, explore the cutting-edge of data analysis with generative AI and advanced dashboards, culminating in a project that brings big data to life through interactive 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 with SQL and SQL table relations
• Utilize programming techniques to process large data samples with large-scale data processing like PySpark with big data

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Inferential Statistics

In this course you will perform statistical inference with Python. This course equips you with the foundational theory and practical skills to analyze data. Learn about probability distributions, confidence intervals, hypothesis testing, and more. Apply these techniques to single proportions, means, and categorical data. Explore advanced methods for two or more groups and tackle multivariate datasets. This culminates with your final project where you’ll showcase your ability to use a multivariate dataset and perform a myriad of the appropriate methods of statistical inference.

What you’ll learn:

• Integrate statistical inference of data using the technical programming
• Implement methodologies for statistical inference
• Utilize mathematics, statistics, & probability for data science methodologies to derive insights

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Regression

This course equips you with the skills to tackle real-world datasets with regression. Master linear regression, exploring diagnostics to ensure model validity. Delve into multiple linear regression, learning to evaluate, diagnose, and leverage its predictive power. Discover advanced techniques like transformations, interactions, and model selection. Explore bias-variance tradeoff and master regularization methods like Lasso and Ridge regression. Finally, in the culminating project, showcase your expertise by building and interpreting a powerful multiple linear regression model.

What you’ll learn:

• Perform logistic regression with data sets using programming techniques, lasso and ridge
• Compare statistical results for different types of regression with data sets, linear, transformations of linear, and multiple linear regressions
• Utilize mathematics, statistics, & probability for data science methodologies to derive insights

Introduction to Machine Learning

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, & probability for data science methodologies to derive insights

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Machine Learning with Scikit-Learn

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, & probability for data science methodologies to derive insights

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Natural Language Processing, Time Series & Neural Networks

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, & probability for data science methodologies to derive insights

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‍Neural Networks & Similar Models

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, & probability for data science methodologies to derive insights

Large Language Models

This course equips you with the skills to deploy and optimize cutting-edge machine learning systems in real-world scenarios. You will explore the open-source MLOps stack, learning to manage the entire ML lifecycle, including deployment, monitoring, and version control. The course emphasizes data-centric approaches for enhancing the performance of large language models (LLMs) through high-quality data curation and preprocessing. You will also master techniques for fine-tuning pre-trained models and leveraging prompt engineering to optimize output for specific tasks. By the end of the course, you will be adept at integrating and maintaining advanced AI solutions in dynamic environments.

What you’ll do:

• Utilize machine learning models and the open-source MLOps Stack
• Integrate data-centric LLMs with data science methodologies to derive insights
• Leverage model fine-tuning and prompt engineering to optimize business solution oriented outputs

Prerequisites:

• Course: Introduction to Machine Learning
• Course: Machine Learning with Scikit-Learn
• Course: Natural Language Processing, Time Series & Neural Networks

AI Capstone 

In this intensive course you will be tasked with developing 2 different projects. In these projects you will be expected to frame your projects around solving a business problem. You will be expected to bring all your skills from Foundations together to build 2 different methods: a classification supervised model, and a classified unsupervised large language model.

What you’ll do:

• Integrate data science process using at least one method of non-regression supervised learning
• Integrate data science process using at least one method of non-regression unsupervised learning
• Utilize mathematics, statistics, & probability for data science methodologies to derive business insights

Prerequisites:

• Course: Introduction to Machine Learning
• Course: Machine Learning with Scikit-Learn
• Course: Natural Language Processing, Time Series & Neural Networks
• Course: Neural Networks & Similar Models
• Course: Large Language Models

You’ll receive a certificate of completion upon passing all programs. Once you complete Capstone — you’ll be ready to jump head-first into the job search with 180 days of career support.