AI and Machine Learning: Code, Train, & Deploy

• Introduction to Data Science

• Python Review

• Variables and Data Types

• Conditional Statements and Loops

• Functions and Modules

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• Introduction to Pandas

• Loading Data with Pandas

• Data Manipulation with Pandas

• Aggregating and Grouping Data with Pandas

• Data Cleaning and Preprocessing with Pandas

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• Introduction to Databases

• SQL Review

• Introduction to APIs

• Accessing Web APIs with Python

• Processing JSON Data

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• Working with a real-world dataset using Pandas and Python

• Data Cleaning and Preprocessing

• Exploratory Data Analysis

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• Descriptive Statistics

• Probability Theory

• Common Probability Distributions

• Statistical Inference

• Hypothesis Testing

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• Introduction to Experimental Design

• Types of Experimental Designs

• Sampling Techniques

• Power Analysis

• A/B Testing

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• 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)

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• Working with a real-world dataset using Python

• Data Cleaning and Preprocessing

• Exploratory Data Analysis

• Data Visualization

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• Introduction to Classification

• Logistic Regression

• Decision Trees and Random Forests

• Naive Bayes

• Model Selection and Evaluation

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• Introduction to Scikit-Learn

• Supervised Learning

• Unsupervised Learning

• Model Selection and Evaluation

• Putting it All Together: Real-World Machine Learning

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• Working with a real-world dataset using Scikit-Learn

• Data Cleaning and Preprocessing

• Feature Engineering

• Model Selection and Evaluation

• Model Deployment

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• Introduction to Neural Networks

• Implementing Neural Networks using TensorFlow and Keras

• Training Deep Learning Models

• Hyperparameter Tuning

• Model Deployment

• Introduction to Prompt Engineering

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• Time Series Analysis

• Natural Language Processing

• Reinforcement Learning

• Ethical AI and Bias in Machine Learning

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• Introduction to ML Ops

• Model Versioning and Reproducibility

• Continuous Integration and Deployment (CI/CD) for ML

• Model Monitoring and Maintenance

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• Finalizing Project Report

• Presentation of Findings

• Feedback and Iteration

• Career Pathways and Job Readiness

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