ML Maths Basics
Understand linear algebra, probability, and calculus concepts used in ML.
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After this skill you can…
- Manipulate vectors and matrices
- Understand gradient descent intuitively
- Apply Bayes' theorem and basic probability
Watch (10 videos)
Machine Learning with Rust and Candle: Part 3
→ Build VAE models→ Generate images with VAEs→ Understand Candle library basics
Bigger Models Fit Smoother, Not Harder
→ Understand the relationship between model size and error→ Identify the sweet spot in the error curve→ Apply gradient descent to find the gentlest fit
Machine Learning Rust Candle Hugging Face Part 4
→ Understand linear algebra concepts→ Apply mathematical concepts to machine learning
The Test Is Right 99% of the Time
→ Understand the concept of base rate→ Apply statistical reasoning to real-world problems
AI Engineer Roadmap 2026 | How To Become An AI Engineer In 2026 | SCRUM Master Skills | Simplilearn
→ Understand machine learning fundamentals→ Apply mathematical concepts to machine learning→ Develop skills in Scrum master
Machine Learning Rust Candle Hugging Face Part 1
→ Create a new Cargo project→ Add dependencies to a Cargo project→ Perform matrix multiplication using tensors
Quant Interview Question #quant
→ Calculate probabilities of geometric events→ Apply probability theory to solve problems
Type I vs Type II Error - Which Mistake Are You Choosing?
→ Understand Type I and Type II errors→ Distinguish between alpha and beta values
Quant Interview Question #quant
→ Calculate probabilities in a random walk→ Apply stochastic processes to solve problems
How Netflix Handles Large Imbalanced Datasets | Machine Learning Case Study Explained
→ Understand the importance of handling imbalanced datasets→ Apply matrix factorization to address class imbalance
DeepCamp AI