Statistics & Linear Algebra for Machine Learning

🎉 Statistics & Linear Algebra for Machine Learning 📚✨

Math Behind ML Algorithms | Linear Algebra | Hypothesis Testing | ANOVA

📈 Testimonials about the Course

• "Great course. It cleared all my doubts. I learned statistics previously from HK Dass sir's book, but I couldn't understand there relationship in data science and machine learning. Loved this course!" - Rubayet A.
• "Simply amazing course where every basic is described clearly and precisely. Go for this course." - Dipesh S
• "Es claro, preciso en los datos. Las ilustraciones son muy pedagógicas, sobre todo las analogías." - Héctor Marañón R.
• "Good for beginners like me to learn the concepts of Machine Learning and the math behind it. Great to review this course again. Thanks." - Clark D
• "Excelentes conceptos, enfocados hacia las investigación de base científica" - Oscar M

🚀 Background and Introduction

SeaportAi, your guide and AI expert, has noticed a prevalent issue: many students and young professionals delve into machine learning without grasping the fundamental mathematical concepts that underpin it. 🧮⚫️

This course aims to bridge that gap, providing you with a comprehensive understanding of the statistics and linear algebra essential for a successful career in artificial intelligence. By mastering these core concepts, you'll build a strong foundation for your journey into AI. 🏗️🚀

📊 Course Overview

This course teaches you the concepts of mathematics and statistics but from an application perspective. It's not just about knowing the concepts—it's about understanding how and why they apply to machine learning. Without this practical knowledge, effectively utilizing and deploying machine learning algorithms remains a challenge. 🌟

You will immerse yourself in topics such as:

• Measures of Central Tendency vs Dispersion
• Mean vs Standard Deviation
• Percentiles
• Types of Data (Quantitative, Qualitative)
• Dependent vs Independent Variables
• Probability (Discrete and Continuous)
• Sample Vs Population
• Hypothesis Testing (One-sample t-test, Z-test, Chi-square test)
• Concept of Stability in statistical analysis
• Types of Distribution (Normal distribution, Binomial distribution, etc.)
• Outliers and their impact on data analysis
• Maths behind machine learning algorithms like regression, decision trees, kNN
• Gradient Descent optimization technique
• Arrays and their operations
• Vectors, including row vectors and column vectors
• Dot product and its significance
• Magnitude of vectors
• Eigenvector and Eigenvalue decomposition
• Cosine Similarity and its application in ML 🎓

✅ Why This Course?

• Practical Approach: Learn concepts through practical examples and applications.
• Clear Explanations: Engage with content that breaks down complex mathematical concepts into digestible, easy-to-understand pieces.
• Real-World Application: Discover how to apply mathematical principles to real-world machine learning scenarios.
• Comprehensive Coverage: From basic to advanced topics, this course covers the full spectrum of math in ML.
• Interactive Learning: Benefit from a mix of video lectures, exercises, and real-life case studies.

Join SeaportAi in this enlightening journey through statistics and linear algebra, the cornerstones of machine learning. 🔍🤖 Enroll now to elevate your data science and AI skills to new heights! 🚀✨