You want to break into the world of Machine Learning. Which models should you learn first?

You can start with the following models in the defined sequence 1. Linear Regression 2. Logistic Regression 3. Decision Trees 4. Random Forests 5. K-Nearest Neighbors (KNN) 6. Naive Bayes 7. Support Vector Machines (SVM) 8. Basic Neural Networks

Basically, Linear regression can be said as one of the most basic and most commonly used models in machine learning. Also, It is a supervised learning model which means the model is trained using a labeled dataset to establish relationships between a dependent variable and one or more independent variables. It uses the concept of statistical methods for determining the best-fitting line through the data points, to reduce the sum of squared residuals. This line serves as a predictive model, facilitating forecasts for continuous numeric outcomes. In today's world, this model is used in situations such as price prediction based on features such as square footage and bedrooms.

Start your machine learning journey with linear regression due to its simplicity, interpretability, and foundational role in understanding regression problems. Learning it first helps you with essential skills for data exploration and visualization, while its practical applications across various domains offer real-world problem-solving experience. Additionally, linear regression serves as a stepping stone to more complex models, making it an ideal starting point in the pursuit of machine learning proficiency.

Understand linear regression inside out. LEarn what is the learning rate and how it affects convergence, learn what the weights really mean, they are gradients, learn what is regularization. Linear regression basically holds the fundamentals needed to understand even deep learning

You should start with a basic regression model -linear regression , then a classification model like logistic regression (debatable whether its classification or regression but for simplicity lets call it for classification), then a tree based like random forest adnd xgboost and finally a clustering technique like k means and dbscan . this will help you cover most of the basic model before venturing into deep learning side of the world

Another widely used ML model is Logistic regression. It is a classification algorithm that also lies under the supervised learning model. It is commonly used for binary classification problems. It's relatively easy to understand and more accurate than linear regression classification. It calculates the probability that a given input belongs to a particular category using the concept of the logistic function. This model is widely used for tasks like determining whether an email is spam or whether a customer will spend money. It is frequently applied in a variety of fields, including marketing, finance, and medical diagnosis.

Following my initial foray into linear regression, I delved into logistic regression through a project analyzing university admissions results based on two test scores. This experience was part of my continued learning path, where I applied logistic regression to classify candidates into admitted and not admitted categories. By working with real application data, I learned how to model binary outcomes and grasp the concept of the sigmoid function, which is pivotal in logistic regression for handling classification problems. This project not only enhanced my understanding of how different regression models are applied in varying contexts but also underscored the importance of choosing the right model based on the nature of the data

Despite its name, logistic regression is a classification algorithm used for binary classification tasks. It's prevalent in fields like medicine (e.g., predicting disease presence) and marketing (e.g., customer churn prediction). Learning logistic regression provides insight into probability estimation and the concept of odds ratios, essential for understanding more advanced classification techniques like neural networks.

Logistic regression is used for classification, for example binary outcomes (spam detection: spam or not spam) or diagnosing diseases (sick or healthy). It estimates probabilities using a logistic function; the output lies between 0 and 1, making it suitable for estimating the likelihood of belonging to a particular class. This uses odds and log-odds, the basis of classification models. It's used in healthcare for patient classification, marketing for customer segmentation, and finance for credit scoring.

Despite its name, it's used for classification problems, not regression. It's essential for binary classification tasks, such as spam detection or predicting whether an event will occur -Used for binary classification tasks to predict the probability of a categorical outcome. -Applies a logistic function to model the probability that an instance belongs to a particular class. -Effective for spam detection, disease diagnosis, and other binary decision-making problems. -Provides the odds ratio, giving insight into the strength of the predictors. -Despite its name, it's a classification algorithm, not a regression technique

The decision tree model is one of the most popular and widely used ML algorithms. They can used for both classification and regression tasks. It is a flowchart-style tree structure, with each internal node representing a feature, each branch representing a decision based on that feature, and each leaf node representing the outcome. They're especially useful for data exploration because they reveal insights into feature importance and how the model makes decisions. If you want to work on some advanced models then this can be one the best models you can work on.

Decision trees are a popular machine learning algorithm used for both classification and regression tasks. They work by recursively splitting the dataset into subsets based on the feature that best separates the data. This process continues until a stopping criterion is met, such as reaching a maximum tree depth or having all data points in a subset belong to the same class. They are easy to interpret and visualize, making them useful for understanding how a model makes decisions. They can handle both numerical and categorical data, and they require little data preprocessing. Additionally, decision trees can capture non-linear relationships in the data, making them flexible and powerful for a variety of tasks.

A model that uses a tree-like graph of decisions and their possible consequences; it's intuitive and useful for both regression and classification problems -Splits data into branches to form a tree structure based on decision rules. -Can be used for both classification and regression tasks, offering versatility. -Intuitive and easy to visualize, helping in understanding how decisions are made. -Handles both numerical and categorical data, making it flexible for various datasets. -Prone to overfitting, especially with complex trees, but can be controlled with pruning.

Learn and Practice Random Forest. This powerful ensemble method combines multiple decision trees for improved accuracy and robustness. It's a versatile tool for both classification and regression.

Decision trees are intuitive models that mimic human decision-making processes. They're easy to understand and interpret, making them valuable for explaining model predictions. Decision trees are used in various applications, including healthcare (diagnosis), finance (credit risk assessment), and marketing (customer segmentation). Mastering decision trees lays a solid foundation for learning ensemble methods like random forests and gradient boosting.

Well, k-means was my introduction to the world of ML, so it's really close to my heart. Since then I've gone on to create my own distance measures and use its variants, such as Fuzzy C-means, etc. K-means clustering is used for partitioning a dataset into a set of distinct, non-overlapping subgroups, or clusters. The goal is to organize the data points into groups based on similarity. The outcome is a set of 'k' clusters that are as compact and separated as possible. Each cluster is defined by its centroid (mean) and includes all data points that are closer to that centroid than to any other. However, it has limitations, such as sensitivity to the initial choice of centroids and difficulty in clustering data of varying sizes and density.

K-means is unsupervised machine learning algorithm used for partitioning a dataset into a predefined number of clusters. A simple yet very powerful model. You will amazed by results of this model which are straight forwards and easy to comprehend insights. But deciding the optimal number of clusters before applying this model is essential. Elbow Method helps find reasonable number of cluster to partition the dataset in. It works by plotting the within-cluster sum of squares (WCSS) against the number of clusters, and then identifying the "elbow" point where the rate of decrease in WCSS slows down significantly. The idea is to choose the value of k at the point where adding more clusters does not significantly improve the clustering quality.

My exploration of machine learning models led me to utilize k-means clustering, an unsupervised learning technique, to identify faulty chips in electronic devices. This project involved analyzing manufacturing data to cluster chips based on various features indicative of performance and quality. By applying k-means, I was able to segment the chips into distinct groups, with one cluster revealing characteristics typical of faults. This approach was particularly enlightening because it highlighted the utility of unsupervised learning in scenarios where predefined labels are absent. Through iterative refinement of the number of clusters and careful analysis of the cluster characteristics, I was able to efficiently isolate the faulty chips.

K-means clustering is an unsupervised learning algorithm used for clustering similar data points into groups. It's widely applied in customer segmentation, image compression, and anomaly detection. Understanding K-means introduces key concepts like centroids and cluster inertia. It's a stepping stone to more sophisticated clustering techniques like hierarchical clustering and Gaussian mixture models.

K-Means Clustering groups data points into clusters based on similarity. This model helps understand how to analyze and find patterns in datasets without predefined labels to discern structure within data. It’s used in market segmentation, where businesses can identify customer groups with similar behaviors or preferences and through image color quantization in data compression. K-Means builds the foundation for more complex clustering techniques and dimensionality reduction, which is essential for large unstructured data.

Neural networks are powerful models inspired by the human brain's structure. They excel in complex pattern recognition tasks, including image and speech recognition, natural language processing, and reinforcement learning. Learning neural networks involves grasping concepts like activation functions, backpropagation, and model architectures (e.g., convolutional neural networks, recurrent neural networks). Neural networks are at the forefront of AI research and are essential for mastering modern machine learning.

I strongly suggest working with neural networks. Inspired by the human brain, neural networks are complex machine learning models that learn from data. These interconnected nodes process information like the brain, allowing them to tackle tasks like image recognition and handle various data types. Of course, their power comes at a cost, as they often require significant data and computational resources for training.

Neural Networks are made up of layers with interconected nodes ('neurons') that learn complex patterns through backpropagation and gradient descent. This models are used for image recognition like CNNs and RNNs and for NLP. NN are used for decision making.

Neural Networks, inspired by the human brain's structure, are powerful models capable of learning complex patterns and relationships from data. They consist of interconnected layers of artificial neurons that process input data through weighted connections, applying nonlinear activation functions to introduce complexity and enable learning of nonlinear relationships. Neural Networks are incredibly versatile, capable of handling various tasks such as classification, regression, and even unsupervised learning tasks like clustering and dimensionality reduction. Deep Neural Networks, with many hidden layers, have revolutionized fields like computer vision, natural language processing, and speech recognition.

The article summarizes some of the machine learning techniques, however applying machine learning models to a (clean) dataset is getting easier and easier. With AutoML options you can evaluate multiple models for your dataset very quickly. In my opinion it is more important to understand the limitations of each ML model. ML Models should be selected considering the constraints of your data, deployment environment, and organization. If you understand the limitations, you can select the best model for your application. Here are three questions that you should answer while keeping the ML models limitations in mind. 1. How much clean data you can get? 2. How are the users going to use the model? 3. How can I explain the results of my model?

In addition to mastering specific machine learning models, it's crucial to consider broader aspects such as data preprocessing, feature engineering, model evaluation, hyperparameter tuning, model interpretability, ensemble methods, deep learning, model deployment, ethical considerations, and continuous learning. These factors collectively contribute to building robust and effective machine learning solutions, ensuring optimal performance, reliability, interpretability, and ethical responsibility in tackling diverse real-world challenges.

Quick few points 1. Before get into ML, first you gain a good grasp on data and their processing and analysis. 2. Revise core math topics of ML I.e statistics and probability, calculas (or mathematical optimization), and linear algebra 3. Go through both theory (mathematical) and code (practical examples) at beginning and to understand what is happening at backend 4. Make basic and foundation strong then switch on harder topics. (Eg. Get good grasp on ML techniques before get into neural nets). Use step by step approach for learning instead doing all things parallelly.

Always complete the prerequisites required to get started with machine learning. Once you are good to go, in every tutorial or bootcamp they will start with linear regression statistics model and it's mathematical intuition so you can get a basic overview on how machine learning model works in the backend with the data. Then go through all the supervised and unsupervised learning algorithms And once you are good and bagged any jobs or your have achieved your goal go for the advanced learning algorithms or hybrid algorithm by researching and get along with the current learning curve

I guess I would go according to my need. For eg Linear Regression: For understanding regression analysis. Logistic Regression: Essential for binary classification problems. Decision Trees: Intuitive for both classification and regression tasks, decision trees are easy to interpret and understand, making them valuable for beginners. Naive Bayes: Based on Bayes’ theorem, is used for text classification and other tasks where the independence assumption holds. Clustering Algorithms (e.g., K-Means): clustering algorithms help identify patterns. Neural Networks (Starting with Feedforward Neural Networks): As the backbone of deep learning, understanding neural networks lays the groundwork for understanding today’s more complex AI advancements.