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 👉 Part 11: Introduction to Machine Learning👈 Welcome back to the Beginner's Guide to Data Science! In this installment, we'll dive into the exciting world of Machine Learning (ML). Machine Learning is a subset of artificial intelligence that enables systems to learn from data and make predictions or decisions without explicit programming. It plays a crucial role in modern data science, powering various applications like recommendation systems, image recognition, natural language processing, and much more. What is Machine Learning? Machine Learning can be understood as a process in which algorithms learn patterns and relationships from data to make informed decisions or predictions. There are three main types of Machine Learning: Supervised Learning : In this type, the algorithm learns from labeled data, where each example has a corresponding target variable. The goal is to learn a mapping from input features to the target variable, enabling the algorithm to predict the targe...

  👉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...