Day 11 – 20: Statistics
After a decent understanding of linear algebra and its operations, it’s time to move forward one step ahead with Statistics in order to deal with data. Having good knowledge of Stats will eventually help in Data analysis, modeling, and evaluation in your journey of machine learning. There are numerous applications of statistics in machine learning such as Data exploration and preprocessing, Feature selection, Model selection and evaluation, uncertainty estimation, etc. So let’s dive into the core of statistics:
- Mean, Standard Deviation, and Variance — Implementation
- Descriptive Statistics
- Descriptive and Inferential Statistics
- Probability Theory and Distribution
- Normal Distribution
- Binomial Distribution
- Uniform Distribution
- Types of Sampling Distribution
- Degrees of Freedom
- Z-Test
- t-Test
- Chi-Square Test
- Linear Regression
- Sample Error and True Error
- Bias Vs Variance and Its Trade-Off
- Hypothesis Testing
- Confidence Intervals
- Correlation and Covariance
- Correlation Coefficient
- Covariance Matrix
- Pearson Correlation
- Spearman’s Rank Correlation Measure
- Kendall Rank Correlation Measure
- Robust Correlations
- Maximum Likelihood Estimation
100 Days of Machine Learning – A Complete Guide For Beginners
Machine learning is a rapidly growing field within the broader domain of Artificial Intelligence. It involves developing algorithms that can automatically learn patterns and insights from data without being explicitly programmed. Machine learning has become increasingly popular in recent years as businesses have discovered its potential to drive innovation, improve decision-making, and gain a competitive advantage.