What machine learning algorithms should you learn?

Begin with foundational algorithms like Linear Regression and Logistic Regression. Progress to Decision Trees, Random Forests, and Gradient Boosting for ensemble methods. Learn Support Vector Machines (SVM) for classification, K-means for clustering, and Principal Component Analysis (PCA) for dimensionality reduction. Dive into Neural Networks, including Convolutional Neural Networks (CNN) for image analysis and Recurrent Neural Networks (RNN) for sequential data. Familiarize with Naive Bayes for text classification. Focus on algorithms relevant to your domain and stay adaptable as new techniques emerge.

One of the most useful set of algorithms but mostly overlooked in learning is Time-Series Forecasting. With rise in IIoT and Digitization of industrial automation Time-series algorithms are often required, however people with solid skills in time-series are hard to find. During your AI journey consider time-series forecasting methods equivalent to supervised/unsupervised learning in terms of efforts. Do a lot of projects from diverse sectors to strengthen your skills.

1. Linear Regression: A basic algorithm used for regression tasks to model the relationship between a dependent variable and one or more independent variables. 2. Logistic Regression: Widely used for binary classification tasks, logistic regression models the probability of a binary outcome. 3. Decision Trees: Used for both classification and regression tasks, decision trees partition the data into subsets based on the feature values to make predictions. 4. Random Forest: An ensemble method that combines multiple decision trees to improve predictive performance and reduce overfitting.

1. Linear Regression: Master the basics. Linear Regression is the bedrock of predictive analytics. Understand its nuances, and you'll wield predictive powers like never before. 2. Random Forests: Level up by exploring Random Forests. This ensemble of Decision Trees forms a powerhouse, combining diverse perspectives to make robust predictions.

Start with foundational algorithms like linear regression, decision trees, and k-nearest neighbors for basic understanding. Progress to more advanced methods such as support vector machines, random forests, and gradient boosting. Include neural networks, especially deep learning models, for complex tasks. Familiarity with clustering algorithms like K-Means and dimensionality reduction techniques like PCA is valuable. Explore reinforcement learning for sequential decision-making and NLP models like BERT and GPT for text-related tasks. Finally, understand ensemble methods to combine models for improved performance. Mastering this spectrum equips you for diverse machine learning applications.

k-Nearest Neighbors (k-NN): A simple instance-based algorithm used for classification tasks by finding the k-nearest neighbors to a given data point and making predictions based on the most common class among its neighbors.

There are several approaches to semi-supervised learning, some of which include: 1. Self-training: The algorithm starts with a small set of labeled data and makes predictions on the unlabeled data. The most confident predictions are added to the labeled set, and the process iterates until convergence. 2. Co-training: Two or more models are trained independently on different feature subsets or views of the data. The models then exchange information by labeling a portion of the unlabeled data and sharing their predictions with each other. 3. Graph-based methods: Constructing a graph representation of the data, where nodes are data points, and edges represent relationships between them.

Popular algorithms used in reinforcement learning include: 1. Q-Learning: An off-policy, model-free RL algorithm that estimates the Q-values and learns an optimal policy. 2. Deep Q-Networks (DQN): An extension of Q-learning that uses deep neural networks to approximate the Q-values, making it suitable for handling high-dimensional state spaces. 3. Policy Gradient Methods: Directly optimize the policy by updating its parameters to maximize the expected cumulative reward. 4. Proximal Policy Optimization (PPO): A popular policy gradient method that stabilizes the training process and prevents large policy updates.

1. Define the problem: Clearly understand the problem you want to solve and the goals you want to achieve. 2. Analyze the data: Examine the characteristics of your dataset, such as the size, dimensionality, and data types. 3. Consider the data size: Some algorithms may not scale well with large datasets. 4. Explore the data distribution: Understanding the distribution of the data can help you choose an algorithm that is suitable for the data's characteristics. 5. Consider interpretability: If interpretability is crucial in your application (e.g., in medical or financial domains), you might prefer simpler models like linear regression or decision trees over more complex black-box models like deep neural networks.

Be iterative in your learnings. 1. There are going to be several variations of the algorithms (for example CNN). Learning all variations can be overwhelming, but make sure you experiment with as much algorithms and variations as possible. 2. Build iterative projects, always focus on improvements and revisit your old projects to see how it could be done better. This will also give you a perspective of the progress you have made so far. 3. Learning an algorithm is only useful when you are able to apply the algorithm to real-world problems. Apply your learning to various projects and build a profile showcasing your strengths.

It's always best to start with supervised learning for a new project and scale or construct new learning models from the results.