MACHINE LEARNING FROM SCRATCH PYTHON- TABLE OF CONTENT

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.

PYTHON 3 FREE COURSE !! ENROLL NOW

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 !!

TABLE OF CONTENT

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. DIFFERENCE BETWEEN MACHINE LEARNING & AI

Machine Learning is a subset of AI. That is, all machine learning counts as AI, but not all AI counts as machine learning. Machine learning refers to the system that can learn by themselves.

6. PERFORMANCE METRICS: CLASSIFICATION MODEL

we are going to talk about 5 of the most widely used Evaluation Metrics of the Classification Model. Before going into the details of the performance

7. PERFORMANCE METRICS: REGRESSION MODEL

we are going to discuss Performance Metrics, and this time it will be Regression model metrics. As in my previous blog, we have discussed Classification …

SUPERVISED ALGORITHM

8. 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.

9. 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.

10. 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.

11. DECISION TREE FROM SCRATCH

We use decision trees for classification and regression. We break down a data set into smaller and smaller subsets while at the same time we develop an associated decision tree incrementally.

12. 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.

13. KNN FROM SCRATCH PYTHON

K nearest neighbors or KNN algorithm is non-parametric, lazy learning, the supervised algorithm used for classification as well as regression. KNN is often used when searching for similar…

14. SVM FROM SCRATCH PYTHON

K nearest neighbors or KNN algorithm is non-parametric, lazy learning, the supervised algorithm used for classification as well as regression. KNN is often used when searching for similar…

UNSUPERVISED ALGORITHM

15. KMEANS FROM SCRATCH PYTHON

In this article, we will cover k-means clustering from scratch. In general, Clustering is defined as the grouping of data points such that the data points in a …

More tutorials coming soon…………

DOWNLOAD AI EBOOK NOW !!