Machine Learning: From Mathematical Foundations to Apps

About this course

Master machine learning from core mathematical foundations to real-world applications across web, mobile, and desktop platforms. This course bridges theoretical concepts with practical implementation, taking you on an end-to-end journey to build, deploy, and scale intelligent AI systems.

You will begin by establishing essential math skills for data science, covering key principles required to understand modern machine learning algorithms. From there, gain hands-on experience using NumPy for efficient numerical computing, alongside techniques for data collection, web scraping, automation, data manipulation, and cleaning.

As you progress, master Exploratory Data Analysis to uncover insights, followed by feature engineering and feature selection to optimize model performance. You will dive deep into main AI subdomains and core machine learning paradigms, including supervised and unsupervised learning techniques.

Finally, turn theory into application by building end-to-end projects. Learn how to integrate machine learning models into web and mobile apps to process numerical, image, and time series data. Advance your skill set with real-time streaming using Apache Kafka, Big Data AI integration, recommendation systems, and computer vision.

Chapter 1: Math for Data science and AI Chapter 2: Numerical Computing with NumPy Chapter 3: Data Collection, Data Manipulation, and Data Cleaning Chapter 4: Exploratory Data Analysis Chapter 5: Features Engineering and Features Selection Chapter 6: AI Subdomains and Machine Learning Chapter 7: Linking AI models with mobile and web apps Chapter 8: Advanced Projects

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