Introduction to Machine Learning

The session introduces learners to what machine learning is, the types of machine learning, the mathematics needed to get there, and what next if one wants to embark on that journey

Transcript

1. Introduction to Machine Learning Wesley Kambale @weskambale kambale.dev
2. • Machine Learning Engineer with 3 years of experience • Community Builder for 3 years • Explore ML Facilitator with Crowdsource by Google for 2 years • Consultant at The Innovation Village • Google Dev Library Author Profile Interests Experience • Research in TinyML, TTS & LLM
3. What You Need to Know/Have? - Knowledge of Python, R, Java, etc - Basic mathematical knowledge (probability and statistics) - Notebook (Google Colab or Jupyter) - Basic data analytics knowledge (MS Excel, Power BI) Pre-requisites
4. Machine Learning… What is Machine Learning? Machine learning (ML) is a subfield of artificial intelligence (AI) that enables computers to learn from data to make predictions and identify patterns. Computers traditionally rely on explicit programming. Machine learning is programming computers to optimize a performance criterion using example data or past experience Machine learning algorithms can be divided into two main categories: supervised and unsupervised learning.
5. Supervised learning What is Supervised Machine Learning? Used when the training data includes labeled examples. The algorithm attempts to find the relationship between the input features (independent variables) and the output (dependent variable), which is known as the "ground truth". Once the relationship has been learned, the algorithm can use this knowledge to make predictions on new, unseen data. Common examples of supervised learning include classification (determining the class of an object based on its features) and regression (predicting a continuous value).
6. Unsupervised learning What is Unsupervised Machine Learning? Used when the training data is unlabeled. The algorithm must identify patterns and structure in the data on its own. Common examples of unsupervised learning include clustering (grouping similar data points) and dimensionality reduction (reducing the number of features in the data).
7. Getting Started Shall we?
8. Mathematics for Machine Learning
9. What we need to know… Matrices: Operations, Transpose, Adjoint, Inverse, Determinant, Trace, etc Scalars & Vectors: Operations, Product, Dot & Cross Product System of Linear Equations: Cramer’s rule Linear Algebra & Matrix
10. What we need to know… Statistics: Mean, Variance, Standard Deviation Random variables, parametric & non-parametric methods Distribution, Confidence Interval, Covariance and Correlation Statistics for ML
11. What we need to know… True Error vs Sample Error Bias-Variance Trade Off Hypothesis Testing: T-test, Paired T-Test, P-value, F-Test, Z-test Statistics for ML - Cont’d
12. What is a confusion matrix? Confusion Matrix Positive (1) Negatice (0) Positive (1) TP FN Negative (0) FN TN
13. What we need to know… Bayes’s Theorem for Conditional Probability Probability Distributions: Uniform, Binomial, Poisson, Exponential, Normal Distribution) Distribution Model: Gamma, Beta Probability for ML
14. What we need to know… Chi-Square Test for Feature Selection Student’s t-distribution in Statistics Central Limit Theorem Probability for ML - Cont’d
15. What we need… Partial Derivatives, Advanced Differentiation, Gradient of a Function, Gradient Descent, Higher Order Derivatives Taylor Series Maxima and Minima, Absolute Minima and Maxima Unconstrained Multivariate Optimization Calculus for ML
16. What we need… Lagrange Multipliers Lagrange’s Interpolation Linear Regression in Machine learning Calculus for ML - Cont’d
17. Next… What next? Pandas NumPy Matplotlib & Seaborn Sci-Kit Learn & TensorFlow
18. Machine learning is the future - Robert John, GDE - ML & Google Cloud
19. Thank you! Wesley Kambale @weskambale kambale.dev