Machine Learning, Data Science and Generative AI with Python Course Syllabus

Overview: This comprehensive course is designed to take you from the fundamentals of Python programming to advanced applications in Machine Learning, Data Science, and Generative AI. With a structured, hands-on curriculum, you'll build practical skills through real-world projects and gain lifetime access to all materials. The total time commitment is approximately 16.5 hours, making it ideal for beginners seeking to launch or enhance a career in data science.

Module 1: Introduction to Python for Data Science

Estimated time: 1 hours

• Setting up the Python environment
• Basic Python syntax and data structures
• Variables, data types, and control flow
• Functions and script execution

Module 2: Data Analysis with Pandas & NumPy

Estimated time: 2 hours

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Handling missing data and outliers
• Data manipulation using Pandas and NumPy

Module 3: Data Visualization Techniques

Estimated time: 1.5 hours

• Creating static plots with Matplotlib
• Advanced visualizations using Seaborn
• Interactive visualizations with Plotly
• Visualizing distributions, correlations, and trends

Module 4: Supervised Learning Algorithms

Estimated time: 3 hours

• Implementing Linear Regression
• Understanding K-Nearest Neighbors
• Decision Trees and Random Forests
• Evaluating model performance using accuracy, precision, recall, and F1-score

Module 5: Unsupervised Learning and Natural Language Processing

Estimated time: 4 hours

• Applying K-Means and Hierarchical Clustering
• Dimensionality reduction with PCA
• Text preprocessing and tokenization
• Building spam filters and text classification models

Module 6: Deep Learning with Neural Networks

Estimated time: 3 hours

• Understanding the basics of neural networks
• Implementing Convolutional Neural Networks (CNNs)
• Image classification tasks using deep learning
• Introduction to Generative AI concepts

Module 7: Model Deployment & Best Practices

Estimated time: 1 hours

• Saving and loading machine learning models
• Deploying models for real-world applications
• Best practices in model versioning and monitoring

Module 8: Final Project

Estimated time: 1.5 hours

• End-to-end data science project using Python
• Apply machine learning and visualization techniques
• Deliver a deployable model with documentation

Prerequisites

• Basic computer literacy
• No prior programming experience required
• Willingness to learn and solve problems

What You'll Be Able to Do After

• Use Python for data analysis and manipulation
• Visualize data effectively using Matplotlib, Seaborn, and Plotly
• Build and evaluate supervised and unsupervised machine learning models
• Apply NLP techniques to classify text and filter spam
• Deploy machine learning models and understand Generative AI fundamentals