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Machine Learning in Python | Ajay Tech

Wanted to learn Machine Learning from a live instructor online ? Register for this 45 hour instructor led, online course on Machine Learning in Python. Everything on this page is taught at 1 hour a day on weekdays. This course is designed for absolute beginner's in mind.

A Course in Machine Learning

CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).

Python Online Training

Learn Python from BigClasses. Master the professional way of developing an advanced application with industrial standards. Create and build your own application by exploring Python. This Training will help you be an expert in Python Structures, Dynamic Typing, Various Operators_Assignment, Arithmetic, Relational, Logical, and Bitwise Operators, types of functions, OS Modules, CSV, and PDB.

Machine Learning Course

This course develops the mathematical basis needed to deeply understand how problems of classification and estimation work. By the end of this course, you'll develop the techniques needed to analyze data and apply these techniques to real-world problems

Machine Learning Training Library

Machine learning is all around us and becoming ever more popular. But what is it? To answer that question, get stuck into the selection of content below that is designed to introduce you to the topic and start you on your journey through the world of machine learning.

Introduction to Machine Learning for Coders

Welcome to Introduction to Machine Learning for Coders! taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.

CS109 Data Science

Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.

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