Math for Data science,Data analysis and Machine Learning
Why take this course?
🎓 Math for Data Science, Data Analysis & Machine Learning: Essentials for Success 🚀
Unlock Your Potential in Tech with Essential Math Skills!
Course Overview:
Welcome to our comprehensive course designed to equip you with the essential Mathematical foundations required for Data Science, Data Analysis, Machine Learning, and Artificial Intelligence. Whether you're an engineer, a commerce student, or someone learning Python Programming, this course is tailored to suit your needs at all levels—beginner to advanced.
What You Will Learn:
- 📊 Linear Algebra: Grasp the importance of Linear Algebra and master Types of Matrices, Matrix Operations, Eigenvalues and Eigenvectors, and more.
- 📈 Statistics and Probability: Understand Statistics' role in Data Science, learn about Measures of Central Tendency, Measures of Dispersion, and Probability Theory.
- ✎ Calculus: Explore the Basic Concepts, Differentiation, Integration, and their applications in Data Science.
- ⫫ Geometry: Dive into Euclidean Geometry with its useful terms and concepts.
- 🔧 Set Theory & Algebra: Gain proficiency in Set Theory, Laws of Algebra of Sets, and Functional Concepts.
Course Curriculum:
-
Linear Algebra:
- Importance of Linear Algebra
- Types of Matrices
- Matrix Addition and Properties
- Matrix Multiplication and Properties
- Transpose of Matrices
- Hermitian and Skew Hermitian Matrices
- Determinants: Introduction
- Minors and Co factors in a Determinant
- Properties of Determinants
- Differentiation of a Determinant
- Rank of a Matrix
- Echelon form and its Properties
- Eigenvalues and Eigenvectors
- Gaussian Elimination Method for linear equations
- Cayley-Hamilton Theorem
-
Statistics:
- Introduction to Statistics
- Classification of Data
- Measures of Central Tendency
- Measures of Dispersion: Range, Mean Deviation, Std. Deviation & Quartile Deviation
- Basic Concepts of Probability
- Sample Space and Verbal Description & Equivalent Set Notations
- Types of Events and Addition Theorem of Probability
- Conditional Probability
- Total Probability Theorem
- Bayes' Theorem
-
Calculus:
- Basic Concepts: Functions, Limits, and Continuity
- Derivative of a Function and Formulae of Differentiation
- Differentiation of functions in Parametric Form
- Rolle's Theorem
- Lagrange's Mean Value Theorem
- Average and Marginal Concepts
- Concepts of Maxima and Minima
- Elasticity: Price elasticity of supply and demand
-
Geometry:
- Introduction to Geometry
- Important Terms, Concepts, Results, and Formulae
-
Set Theory & Algebra:
- Definition and Representation of Set Theory
- Type of Sets
- Subset, Power set, and Universe
- Laws of Algebra of Sets
- Important Formulae of Number of Elements in Sets
- Basic Concepts of Functions
- Graphs of Real-valued functions
- Graphs of Exponential, Logarithmic, and Reciprocal Functions
Course Features:
- Simple Explanations: Each topic is explained in a straightforward manner to ensure clarity.
- Real-World Examples: Practical examples are used to solidify your understanding.
- Supportive Q&A Section: Get your questions answered and engage with fellow learners.
- Continuous Improvement: New course materials will be added based on student feedback.
Enroll Now and Elevate Your Mathematical Skills for a Career in Tech! 🌟
We're excited to have you join us and look forward to supporting your journey towards mastering the Math behind Data Science, Data Analysis, Machine Learning, and AI. Let's embark on this learning adventure together! 🚀✨
Loading charts...