AI and Machine Learning Foundations - Python, Data Science, and MLOps

Overview

Master AI and machine learning fundamentals through Python, from basic concepts to advanced algorithms, with hands-on projects in predictive analytics and practical guidance for launching a data science career.

Syllabus

• Introduction to AI and Machine Learning
Overview of AI and ML
Historical Context and Evolution
Current Trends and Applications
• Python for AI and Machine Learning
Python Basics and Setup
Numpy and Pandas for Data Manipulation
Data Visualization with Matplotlib and Seaborn
• Data Science Fundamentals
Data Collection and Cleaning
Descriptive Statistics
Exploratory Data Analysis (EDA)
• Supervised Learning
Regression (Linear, Logistic)
Classification Algorithms (Decision Trees, Support Vector Machines, k-NN)
Model Evaluation Metrics
• Unsupervised Learning
Clustering (k-Means, Hierarchical)
Dimensionality Reduction (PCA, t-SNE)
Anomaly Detection
• Advanced Machine Learning Algorithms
Ensemble Methods (Random Forests, Gradient Boosting)
Neural Networks Basics
Deep Learning Introduction
• Hands-on Projects in Predictive Analytics
Project 1: Predictive Modeling with Regression
Project 2: Customer Segmentation using Clustering
Project 3: Image Classification with Deep Learning
• Introduction to MLOps
Model Deployment Basics
CI/CD Pipelines for Machine Learning
Monitoring and Maintenance of Models
• Practical Guidance for a Data Science Career
Building a Portfolio
Networking and Community Engagement
Interview Preparation and Career Pathways
• Course Summary and Next Steps
Recap of Key Concepts
Additional Resources for Continued Learning
Certifications and Further Education Options

Subjects

Data Science