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Welcome to AI HUB’s new series on “Machine Learning from Scratch”. Here we will include a full Table of contents of Machine Learning from the Scratch tutorial series. Here we will cover all the courses based on Python. If you are new to Python, you can enroll in our free Python course from here.
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Python is widely considered as the preferred language for Artificial Intelligence. Python helps developers be productive and confident about the software they’re building from development to deployment and maintenance. ENROLL NOW !!
Artificial intelligence can be interpreted as adding human intelligence in a machine. The beginning of modern AI started when the term “Artificial Intelligence” was coined in 1956, at a conference at Dartmouth College, in Hanover. The field has come a very long way in the past decade. The 10s were the hottest AI summer with tech giants like Google, Facebook, Microsoft repeatedly touting AI’s abilities.
Here are the direct link of the Machine Learning Algorithm from Scratch. All the best wishes !!
1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Detail about AI, ML and their types : Supervised, unsupervised & Reinforcement learning
2. HISTORY OF ARTIFICIAL INTELLIGENCE
AI winters and current hype and reality about AI
3. BEGINNERS GUIDE TO MACHINE LEARNING
Are you planning to start your career in the field of AI, but having trouble where to give it a go ??
4. KEY TERMS USED IN MACHINE LEARNING
Details on classification, regression, clustering and much more
5. LINEAR REGRESSION FROM SCRATCH
We will build a linear regression model to predict the salary of a person on the basis of years of experience from scratch.
6. LOGISTIC REGRESSION FROM SCRATCH
Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more independent variables.
7. NAIVE BAYES ALGORITHM FROM SCRATCH
Classify from scratch whether a given person is a male or a female based on the measured features. The features include height, weight, and foot size.
Decision tree models can be used for both classification and regression. The algorithms for building trees break down a data set into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed.
9. RANDOM FOREST FROM SCRATCH PYTHON
In this tutorial we will work on Stock Prediction using random forest. Here, we will be using the dataset (available below) which contains seven columns namely date, open, high, low, close, volume, and name of the company.
More tutorials coming soon…………