Lets Summarize!!
- Mean (Average): Sum of all values divided by the number of values.
- Median: The middle value in a list of numbers.
- Mode: The most frequently occurring value.
- Variance: Measure of the spread between numbers in a data set.
- Standard Deviation: Square root of variance, shows how much variation exists from the mean.
- Random Variables: Variables whose values depend on outcomes of a random phenomenon.
- Probability Distribution: A function that describes the likelihood of different outcomes.
- Bayes' Theorem: Describes the probability of an event based on prior knowledge of conditions related to the event.
- Expectation (Expected Value): The weighted average of all possible values a random variable can take.
- Scalars: Single numbers.
- Vectors: Ordered lists of numbers.
- Matrices: Rectangular arrays of numbers.
- Matrix Multiplication: Combining two matrices to produce a new matrix.
- Determinant: A scalar value that can be computed from the elements of a square matrix.
- Eigenvalues and Eigenvectors: Scalars and vectors associated with a matrix that provide insights into its properties.
- Derivatives: Measure how a function changes as its input changes.
- Partial Derivatives: Derivatives of functions with multiple variables.
- Gradient: Vector of partial derivatives, indicates the direction of the steepest increase of a function.
- Integration: Opposite of differentiation, sums up parts to find the whole.
- Gradient Descent: Iterative algorithm for finding the minimum of a function.
- Cost Function (Loss Function): Measures how well a model's predictions match the actual data.
- Convergence: When the algorithm reaches a minimum value of the cost function.
- Normal Distribution: Bell-shaped distribution, also known as Gaussian distribution.
- Binomial Distribution: Describes the number of successes in a fixed number of trials.
- Poisson Distribution: Describes the number of events occurring within a fixed interval.
- Null Hypothesis (H0): The hypothesis that there is no effect or no difference.
- Alternative Hypothesis (H1): The hypothesis that there is an effect or a difference.
- p-value: The probability of obtaining results at least as extreme as the observed results, assuming that the null hypothesis is true.
- Confidence Interval: A range of values, derived from the sample, that is likely to contain the value of an unknown population parameter.
- Linear Regression: Models the relationship between a dependent variable and one or more independent variables.
- Multiple Regression: Extension of linear regression that uses multiple variables to predict the outcome.
- R-squared: Indicates the proportion of the variance in the dependent variable that is predictable from the independent variables.
- Supervised Learning: Algorithms that learn from labeled data.
- Unsupervised Learning: Algorithms that learn from unlabeled data.
- Classification: Predicting a category or class.
- Regression: Predicting a continuous value.
- Clustering: Grouping similar items together.
- Normalization: Scaling data to fit within a specific range.
- Standardization: Scaling data so that it has a mean of 0 and a standard deviation of 1.
- Missing Value Imputation: Filling in missing data points with estimated values.
- Feature Engineering: Creating new features from existing data to improve model performance.
- Cross-Validation: Technique to evaluate the model by splitting the data into training and testing sets.
- Confusion Matrix: A table used to describe the performance of a classification model.
- Precision and Recall: Metrics for evaluating classification models.
- F1 Score: Harmonic mean of precision and recall.
- Principal Component Analysis (PCA): Technique to reduce the dimensionality of the data by transforming to a new set of variables.
- t-Distributed Stochastic Neighbor Embedding (t-SNE): Non-linear dimensionality reduction technique for visualization.
These concepts form the foundation of machine learning and data science. Understanding them is crucial for developing robust and effective models. Happy learning! 🚀📚