10 Machine Learning Algorithms You Should Know to Become a Data Scientist

The article defines a data scientist as an expert who extracts knowledge from data using scientific methods, integrating fields such as signal processing, mathematics, probability, machine learning, programming, statistics, data engineering, pattern recognition, visualization, uncertainty modeling, data warehousing, and high-performance computing.

It then covers key machine learning algorithms: Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) for dimensionality reduction; Ordinary Least Squares (OLS) and polynomial fitting for regression; constrained linear regression techniques like LASSO, ridge, and elastic net to reduce variance and avoid overfitting; K-Means clustering for unsupervised grouping; Logistic regression for classification using sigmoid or tanh functions; Support Vector Machines (SVM) with margin-based loss and kernel tricks; Feedforward neural networks (multilayer perceptrons) for classification and autoencoding; Convolutional neural networks (CNNs) for image and text processing; Recurrent neural networks (RNNs) and their variants LSTM/GRU for sequence modeling; Conditional Random Fields (CRF) for structured prediction; Decision tree ensembles including random forest, gradient boosting, XGBoost, CatBoost; and Temporal Difference (TD) learning algorithms such as Q-learning and SARSA for reinforcement learning.

For each algorithm, the article provides references to relevant libraries (e.g., SciPy, scikit-learn, NumPy, Keras, TensorFlow) and tutorial links for further study.