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  👉Part 9: Introduction to Machine Learning Algorithms👈 Welcome to part 9 of the "Beginner's Guide to Data Science" blog series! In this installment, we will dive deeper into the fascinating world of machine learning and explore different types of machine learning algorithms. Machine learning is a critical aspect of data science, as it allows us to make predictions and decisions based on patterns and trends found in data. Let's get started: 1. Supervised Learning: Supervised learning is a type of machine learning where the model is trained on labeled data. Labeled data means that each input has an associated output or target variable. The goal of supervised learning is to learn a mapping between input features and the target variable so that the model can predict the target for new, unseen data. Examples of supervised learning algorithms: Linear Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Neural Networks. 2. U...

👉 Part 8: Feature Engineering👈 Welcome back to our beginner's guide to data science! In this segment, we'll delve into the intriguing world of feature engineering, a critical aspect of data preprocessing that has a significant impact on the performance of your machine learning models. Understanding Feature Engineering: Feature engineering involves creating new features from the existing ones or transforming the existing features to improve the predictive power of your models. Well-engineered features can uncover hidden patterns in the data, making your models more effective and accurate. Feature Engineering Techniques: Feature Extraction: This involves transforming raw data into a feature space where it can be more effectively used by machine learning algorithms. Techniques like text vectorization (e.g., TF-IDF, word embeddings) and image feature extraction (e.g., using Convolutional Neural Networks) fall under this category. Feature Transformation: Transforming features c...

👉Part 6: Machine Learning Fundamentals👈   Introduction: Welcome to Part 6 of our Beginner's Guide to Data Science series! In this installment, we will dive into the fascinating world of Machine Learning (ML). Machine Learning is a subset of artificial intelligence that empowers computers to learn from data and make predictions or decisions without explicit programming. It is one of the core pillars of data science and has numerous applications in various industries. What is Machine Learning? Machine Learning is a field of study that focuses on developing algorithms and statistical models that enable computers to learn from data and improve their performance on a specific task over time. The process involves training a model on a labeled dataset (input-output pairs) to learn patterns and relationships, and then using that knowledge to make predictions or decisions on new, unseen data. Types of Machine Learning: a. Supervised Learning: In supervised learning, the model is traine...