Essential Maths & Statistics for Machine Learning Course Overview
The 'Essential Maths & Statistics for Machine Learning Course' by Open Source equips professionals with critical mathematical foundations necessary for machine learning. This course is invaluable for data scientists, software engineers, and analysts, enhancing their ability to interpret data, build algorithms, and make informed decisions. Mastering these concepts is essential for success in the evolving field of machine learning.
Course outline & what you'll learn
- Understanding the role of mathematics and statistics in machine learning
Overview of machine learning algorithms
- Vectors and matrices
- Matrix operations and properties
- Eigenvalues and eigenvectors
- Applications of linear algebra in machine learning
- Functions and limits
- Derivatives and gradients
- Optimization techniques
- Partial derivatives and the chain rule
- Basic concepts of probability
- Random variables and probability distributions
- Conditional probability and Bayes' theorem
- Common distributions used in machine learning (Normal, Binomial, Poisson)
- Descriptive statistics
- Inferential statistics
- Hypothesis testing and p-values
- Confidence intervals
- Data normalization and standardization
- Handling missing data
- Feature scaling and transformation techniques
- Understanding the regression model
- Cost function and optimization
- Evaluating regression models
Overview of classification algorithms
- Logistic regression
- Decision trees and random forests
- Support Vector Machines (SVM)
- K-means clustering
- Hierarchical clustering
- Principal Component Analysis (PCA)
- Cross-validation techniques
- Bias-variance tradeoff
- Performance metrics (accuracy, precision, recall, F1 score)
- Recap of essential concepts
- Emerging trends in machine learning mathematics and statistics
- Resources for further learning and exploration
Why train with Traincrest
This Open Source course is delivered by Traincrest's certified instructors, live online or in the classroom, with hands-on labs and a 98% exam success rate. Trusted by 500+ companies and 50,000+ students worldwide.