AI & Machine Learning: Code, Train, Deploy
Whether you're an aspiring data scientist, a data analyst looking to advance, or a professional seeking to transition into AI and Machine Learning roles, our curriculum covers the entire ML lifecycle: from data preprocessing and model training to deep learning, Natural Language Processing (NLP), reinforcement learning, and robust model deployment strategies on cloud platforms. Culminate your learning with a capstone project focused on solving real-world challenges, guided by ethical AI considerations.
Our comprehensive AI and Machine Learning course covers essential skills and frameworks to make you a versatile data professional.
Foundations of Data Science & Programming
- Introduction to Data Science: Understand the complete data science workflow, from problem definition to insights and deployment.
- Python Programming Fundamentals: Master Python, the leading language for AI, Machine Learning, and data analysis. Learn core syntax, data structures, and object-oriented programming.
- Data Manipulation with Pandas: Become proficient in data wrangling, cleaning, and transformation using the powerful Pandas library for efficient data preprocessing.
- Working with Databases & APIs: Learn to extract and manage data from various sources, including SQL databases and external APIs, crucial for real-world AI projects.
Core Machine Learning & Deep Learning
- Classical Machine Learning: Explore supervised and unsupervised learning algorithms, including regression, classification, clustering, and dimension reduction. Build, train, and evaluate machine learning models.
- Deep Learning with TensorFlow: Dive into the world of neural networks. Learn to build and train deep learning models for complex tasks like image recognition and sequence prediction using TensorFlow.
- Natural Language Processing (NLP): Understand techniques for processing and analyzing human language. Develop NLP models for sentiment analysis, text generation, and more.
- Reinforcement Learning: Explore how agents learn to make decisions in dynamic environments through trial and error.
AI Model Deployment & Real-World Applications
- Model Evaluation & Optimization: Master techniques to assess your AI models' performance and optimize them for accuracy and efficiency.
- Cloud Platform Integration: Learn to leverage cloud services for scaling AI/ML workloads and deploying your models for production use.
- Ethical AI Considerations: Understand the importance of responsible AI development, fairness, transparency, and bias mitigation.
- Capstone Project: Real-World AI Application: Apply all your learned skills to develop a significant AI project, from data collection and model training to deployment, showcasing your ability to deliver end-to-end machine learning solutions.
• 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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