Topics that will be helpful for AI Journey

Below are foundations 

- Mathematics and Statistics

- Linear Algebra - Vectors, Matrices, EigenValues and Eigen Vectors (Important for understanding stability and transformations)  

- Calculus - Derivatives - represents the rate of change in a function vital for optimisation 

- Integrals - Calculate accumulated quantities and areas under curve used in probability and statistics 

- Multivariable calculus - Essential for functions with multiple input dimensions 

- Probability and Statistics 

- Binomial, poisson, Normal, Exponential

- Mean and Variance : Measure central tendency and spread of data

- Statistical concepts play a crucial role in machine learning, helping practitioners analyze data, make predictions, and understand the uncertainty associated with predictions. Here are some key statistical concepts in machine learning:

Probability: Probability theory is fundamental to understanding uncertainty in machine learning. It involves the likelihood of events occurring and provides a basis for statistical inference.

Descriptive Statistics: Descriptive statistics, including measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation), are used to summarize and describe the main features of a dataset.

Inferential Statistics: Inferential statistics involves making inferences about a population based on a sample of data. Techniques include hypothesis testing, confidence intervals, and regression analysis.

Probability Distributions: Understanding different probability distributions (e.g., normal distribution, binomial distribution) is important for modeling and analyzing data in machine learning.

Statistical Inference: Statistical inference involves drawing conclusions about a population from a sample. It includes techniques such as hypothesis testing and confidence intervals.

Hypothesis Testing: Hypothesis testing is used to assess whether a hypothesis about a population parameter is supported by the sample data.

Confidence Intervals: Confidence intervals provide a range of values that likely contains the true value of a population parameter, along with an associated level of confidence.

Bayesian Statistics: Bayesian statistics is an approach that incorporates prior beliefs and updates them with new evidence to make probabilistic predictions. Bayesian inference is used in Bayesian models.

Regression Analysis: Regression analysis models the relationship between a dependent variable and one or more independent variables. It helps in predicting the values of the dependent variable.

ANOVA (Analysis of Variance): ANOVA is used to analyze the differences among group means in a sample. It is often used in the context of comparing means across multiple groups.

Correlation and Covariance: Correlation measures the strength and direction of a linear relationship between two variables. Covariance measures how two variables vary together.

Resampling Methods: 

- Programming Fundamentals 

Machine Learning Basics 

- Machine Learning Fundamentals 

- Deep Learning Fundamentals 

- ML and DL frameworks 

References:

https://levelup.gitconnected.com/generative-ai-learning-roadmap-from-absolute-beginner-to-practioner-part-1-f878ad9231c0