# Getting started with DS (curated) Topics to go around : * Basic Probability Distributions z-score Expectation, Variance, and Co-variance Conditional Probability Independance of variables Bias and Variance Central Limit Theorem Confidence Intervals * Exploratory Data Analysis Missing value treatment Observe/discover trends in data Outlier detection and treatment * Graphs and plots Using libraries like matplotlib, seaborn When to use which plot * Hypothesis Testing * ML as a black box Reading about algorithms without diving deep Linear and Logistic Regression Decision Tree Support Vector Machines Naive Bayes k-Nearest Neighbours k-means Clustering Random Forest Classifiers * Understanding Machine Learning Linear Least Squares Curse of Dimensionality One hot encoding Gradient Descent (stochastic and mini-batch) Cosine similarity Kernels in SVM's --- * What is **deep learning** ? Activation Functions Loss Functions Convolution Neural Networks Backpropogation ANN, RNN and CNN meaning and use-cases --- Advanced (for further reading): 1. GUI programming 2. Databases 3. Parallelization of queries 4. Cloud storage and computing --- For further statistical reading : Class notes of Probability and Random Processes (SEM-3) offered by ECE dept ## Beginner Friendly [Classification](https://www.kaggle.com/competitions/classic-classification/overview)