AI and Machine Learning: Code, Train, & Deploy
This comprehensive 16-week course provides a deep dive into the fundamentals and advanced concepts of Artificial Intelligence and Machine Learning. Students will begin with foundational AI/ML principles, Python programming, and essential tools before progressing to data preprocessing, exploratory data analysis, and classical machine learning techniques. The course then covers deep learning fundamentals, including neural networks, CNNs, RNNs, and transformers for NLP. Advanced topics such as reinforcement learning and generative models are explored, followed by practical deployment strategies using MLOps, cloud platforms, and Docker. The course culminates with a hands-on capstone project, integrating ethical considerations and responsible AI development practices.
In this Course, you'll learn the following:
- Fundamentals of AI and machine learning
- Data preprocessing and exploratory data analysis
- Supervised and unsupervised learning techniques
- Deep learning with TensorFlow and PyTorch
- Natural language processing (NLP) and transformers
- Reinforcement learning and its applications
- Model deployment using cloud platforms and MLOps
- Ethical considerations in AI and responsible development
• 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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