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Machine Learning Tutorial - Machine Learning, often abbreviated as ML is a branch of Artificial Intelligence (AI) that works on algorithm developments and statistical models that allow computers to learn from data and make predictions or decisions without being explicitly programmed.
Machine Learning Tutorials - Tutorials for Python Technologies including Concurrency, Machine Learning, Deep Learning, Design Pattern, Artificial Intelligence etc.
This tutorial is a stepping stone to your Machine Learning journey. Learn Python in-depth with real-world projects through our Python certification course. Enroll and become a certified expert to boost your career. Prerequisites. The reader must have basic knowledge of Artificial Intelligence.
This machine learning tutorial helps you gain a solid introduction to the fundamentals of machine learning and explore a wide range of techniques, including supervised, unsupervised, and reinforcement learning.
In this step-by-step tutorial you will: Download and install Python SciPy and get the most useful package for machine learning in Python. Load a dataset and understand it’s structure using statistical summaries and data visualization. Create 6 machine learning models, pick the best and build confidence that the accuracy is reliable.
Here’s how to get started with machine learning algorithms: Step 1: Discover the different types of machine learning algorithms. A Tour of Machine Learning Algorithms; Step 2: Discover the foundations of machine learning algorithms. How Machine Learning Algorithms Work; Parametric and Nonparametric Algorithms; Supervised and Unsupervised ...
Machine learning is a field of computer science that uses statistical techniques to give computer programs the ability to learn from past experiences and improve how they perform specific tasks.
A rapidly developing field of technology, machine learning allows computers to automatically learn from previous data. For building mathematical models and making predictions based on historical data or information, machine learning employs a variety of algorithms.
Chapter 1: What is Machine Learning? Chapter 2: Most popular Machine Learning algorithms. 2.1 Linear Regression and Ordinary Least Squares (OLS) 2.2 Logistic Regression and MLE. 2.3 Linear Discriminant Analysis (LDA) 2.4 Logistic Regression vs LDA. 2.5 Naïve Bayes. 2.6 Naïve Bayes vs Logistic Regression. 2.7 Decision Trees. 2.8 Bagging.
A Machine Learning Tutorial With Examples: An Introduction to ML Theory and Its Applications. This Machine Learning tutorial introduces the basics of ML theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with the topic.