Machine Learning is revolutionizing industries worldwide. This Skill Tree offers a systematic way to learn ML concepts and techniques. Tailored for beginners, it provides a clear roadmap to grasp algorithms, model training, and data analysis. Hands - on, non - video courses and practical exercises in an interactive ML playground help you develop real - world skills in building and deploying machine learning models.
TABLE OF CONTENTS
BASIC CONCEPTS
Basic concepts of machine learning, including foundational knowledge such as data preprocessing, feature engineering, and understanding the machine learning workflow.
Basic Concepts include fundamental knowledge about machine learning, such as data types, algorithms, and key concepts used in the field.
Early stopping is a technique used to prevent overfitting by monitoring the model's performance during training and stopping when it no longer improves on a validation set.
Mini-batch training involves dividing the training dataset into small batches, which are used to update the model's parameters iteratively, making training more efficient.
Dealing with unbalanced data focuses on strategies to address situations where one class in a classification problem has significantly fewer examples than the others.
REGRESSION ALGORITHMS
Regression Algorithms comprises algorithms used for regression tasks, which involve predicting continuous numeric values.
Linear regression is a simple algorithm used for modeling the relationship between a dependent variable and one or more independent variables through a linear equation.
Logistic regression is used for binary classification problems and models the probability of an input belonging to a particular class.
Polynomial regression extends linear regression by fitting a polynomial equation to the data, allowing for more complex relationships to be captured.
DECISION TREE ALGORITHMS
Decision Tree Algorithms encompasses algorithms related to decision trees, which are used for both classification and regression tasks.
A decision tree is a tree-like model used for decision-making, where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome.
INSTANCE-BASED ALGORITHMS
Instance-based algorithms focus on learning from the data instances themselves, making predictions based on similarity measures.
K-Nearest Neighbor (K-NN) is a simple instance-based algorithm that makes predictions by finding the K nearest data points to a given input and aggregating their labels.
Support Vector Machines (SVMs) are powerful algorithms used for both classification and regression tasks, aiming to find the best hyperplane that separates data points.
BAYESIAN ALGORITHMS
Algorithms based on Bayesian statistics, which use probabilistic reasoning for machine learning tasks.
Naive Bayes is a simple probabilistic classifier based on Bayes' theorem with the 'naive' assumption of independence between features.
ENSEMBLE ALGORITHMS
Ensemble algorithms combine multiple models to improve predictive performance.
Bootstrapped Aggregation, or Bagging, is an ensemble technique that involves creating multiple bootstrapped datasets and training models on each to reduce variance.
Boosting is an ensemble technique that combines weak learners into a strong learner, with a focus on correcting the errors of previous models.
ARTIFICIAL NEURAL NETWORK ALGORITHMS
Artificial Neural Networks (ANNs) are computational models inspired by the human brain and are used for various machine learning tasks.
Artificial Neural Networks (ANNs) are computational models inspired by the human brain and consist of interconnected nodes (neurons) organized in layers. ANNs are used for tasks such as classification, regression, and pattern recognition.
Multilayer Perceptrons (MLPs) are a type of feedforward neural network with multiple layers, commonly used for a wide range of tasks.
A Perceptron is a simple neural network unit that makes binary decisions, often used as the building block for more complex neural networks.
Stochastic Gradient Descent (SGD) is an optimization algorithm commonly used to train neural networks by updating the weights with small random batches of data.
DEEP LEARNING ALGORITHMS
Deep Learning focuses on neural networks with multiple layers, enabling the modeling of complex patterns and representations.
Convolutional Neural Networks (CNNs) are deep learning models designed for image and spatial data, using convolutional layers to capture spatial hierarchies.
CLUSTERING ALGORITHMS
Clustering algorithms are a type of machine learning technique used to partition a dataset into groups of objects, where objects within the same group are similar to each other while being dissimilar to those in other groups. This helps in data analysis and pattern recognition by automatically identifying meaningful clusters or structures within the data.
Centroid-based clustering algorithms assign data points to the cluster with the nearest centroid, such as K-Means.
Density-based clustering algorithms group data points based on density regions, as in DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
Hierarchical Clustering builds a tree-like structure of clusters, allowing for a hierarchy of cluster assignments.
K-Means is a popular clustering algorithm that partitions data points into K clusters by minimizing the distance between data points and cluster centroids.
Spectral Clustering uses the eigenvalues of a similarity matrix to partition data into clusters.
REGULARIZATION ALGORITHMS
Regularization techniques are used to prevent overfitting in machine learning models.
Lasso (Least Absolute Shrinkage and Selection Operator) regularization adds a penalty term to the model's loss function, encouraging the model to have fewer non-zero coefficients.
Ridge regression adds a penalty term to the model's loss function, preventing large coefficients and mitigating multicollinearity.
DATA TRANSFORMATIONS
Data transformation techniques are used to modify or prepare data for machine learning.
One-Hot Encoding is a technique to convert categorical variables into a binary matrix, enabling machine learning algorithms to work with categorical data efficiently.
EVALUATION METRICS
Evaluation metrics are used to assess the performance of machine learning models.
Classification accuracy measures the proportion of correctly classified instances in a classification problem.
A Confusion Matrix is a table used to evaluate the performance of a classification algorithm, showing true positives, true negatives, false positives, and false negatives.
Mean Absolute Error (MAE) is a metric used to evaluate regression models, measuring the average absolute difference between predicted and actual values.
Mean Squared Error (MSE) is a metric used to evaluate regression models, measuring the average squared difference between predicted and actual values.
FRAMEWORK AND SOFTWARE
These are popular libraries and frameworks used for machine learning and deep learning.
Keras is a high-level neural networks API that simplifies the development of deep learning models.
PyTorch is an open-source deep learning framework that offers flexibility and dynamic computation graphs.
scikit-learn is a versatile machine learning library in Python, providing a wide range of tools for various tasks.
TensorFlow is an open-source machine learning framework developed by Google, widely used for deep learning applications.