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AI and Machine Learning: Code, Train, & Deploy
Master the end-to-end lifecycle of artificial intelligence and machine learning in Skillspire's comprehensive, 16-week program. Designed to bridge theoretical foundations with rigorous technical execution, this course takes you from foundational Python programming and exploratory data analysis to building, training, and deploying advanced predictive models, neural networks, and modern generative AI tools.
In this program, you will master the end-to-end data science and machine learning lifecycle, starting with essential Python and SQL foundations, data acquisition, and profiling for both structured and unstructured data using cloud platforms like Azure or Google Cloud. You will learn how to build, train, and evaluate classical and advanced predictive models—including Simple and Multiple Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines (SVM), and Neural Networks—while exploring computer vision, natural language processing, and deep learning architectures. Additionally, you will integrate modern AI tools like ChatGPT and Blackbox.ai into your workflows, master model deployment strategies and cloud-based options, address AI ethics, and culminate your studies with an end-to-end real-world capstone project.
• Introduction to Data Science
• Python Review
• Variables and Data Types
• Conditional Statements and Loops
• Functions and Modules
• Introduction to Pandas
• Loading Data with Pandas
• Data Manipulation with Pandas
• Aggregating and Grouping Data with Pandas
• Data Cleaning and Preprocessing with Pandas
• Introduction to Databases
• SQL Review
• Introduction to APIs
• Accessing Web APIs with Python
• Processing JSON Data
• Working with a real-world dataset using Pandas and Python
• Data Cleaning and Preprocessing
• Exploratory Data Analysis
• Descriptive Statistics
• Probability Theory
• Common Probability Distributions
• Statistical Inference
• Hypothesis Testing
• Introduction to Experimental Design
• Types of Experimental Designs
• Sampling Techniques
• Power Analysis
• A/B Testing
• Introduction to Data Visualization
• Introduction to Matplotlib
• Introduction to Seaborn
• Basic Plots and Customizations
• Advanced Plots and Customizations
• Introduction to Linear Regression
• Simple Linear Regression
• Simple Logistic Regression
• Multiple Linear Regression
• Model Selection and Evaluation
• Regularization Techniques (L1, L2, Elastic Net)
• Working with a real-world dataset using Python
• Data Cleaning and Preprocessing
• Exploratory Data Analysis
• Data Visualization
• Introduction to Classification
• Logistic Regression
• Decision Trees and Random Forests
• Naive Bayes
• Model Selection and Evaluation
• Introduction to Scikit-Learn
• Supervised Learning
• Unsupervised Learning
• Model Selection and Evaluation
• Putting it All Together: Real-World Machine Learning
• Working with a real-world dataset using Scikit-Learn
• Data Cleaning and Preprocessing
• Feature Engineering
• Model Selection and Evaluation
• Model Deployment
• Introduction to Neural Networks
• Implementing Neural Networks using TensorFlow and Keras
• Training Deep Learning Models
• Hyperparameter Tuning
• Model Deployment
• Introduction to Prompt Engineering
• Time Series Analysis
• Natural Language Processing
• Reinforcement Learning
• Ethical AI and Bias in Machine Learning
• Introduction to ML Ops
• Model Versioning and Reproducibility
• Continuous Integration and Deployment (CI/CD) for ML
• Model Monitoring and Maintenance
• Finalizing Project Report
• Presentation of Findings
• Feedback and Iteration
• Career Pathways and Job Readiness
Admin Office: 2265 116th Ave NE, Bellevue, WA 98004
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