Supervised Machine Learning In Python

Machine learning, supervised machine learning, how to train your own model, ML algorithms, explained

What you'll learn

Introduction to machine learning.

Introduction to supervised and unsupervised machine learning.

Details of supervised machine learning.

How to apply machine learning concepts to real world data.

How to extract hidden patterns from real world datasets.

Requirements

If you are familiar with just basics of python, you can start this course.

Description

Hi there, welcome to our machine learning course.  In this course we will be explaining fundamentals of machine learning and will dive in the details in one of the category of ML, which is supervised machine learning. Programming language used in this course: PythonThere are 7 sections in this course with total 52 lectures.In first section we have discussed about machine learning and types of machine learning. There is a comprehensive video explaining the concept as well. We have tried our best to explain the concept in simple and understandable language.In second section we trained our first machine learning model. This section contain 20 lectures and we assure that if you take those all 20 lectures, you will have no confusion in machine learning. There are coding, video lectures, written lectures and quizzes for you in this section. We also have covered all rudimentary steps of data science in this section for your clarity. Such as:Exploring the dataset, making your own dataset, and understanding datasets for machine learning etc.In third section we have trained model on another machine learning algorithm (linear regression). Working of linear regression, graphical understanding and basic math behind this algorithm is well explained in video as well in text lectures. There are 8 lectures in this section containing 3 video lectures. We also have added a short assignment for you, increase your confidence on your understandings.In fourth section we have trained a model on famous dataset, which is iris dataset in which we have to guess from which specie flower belong to. We have trained a model which predict flower category based on some features of flower. Things are explained in text as well in video lecture. In fifth section we have discussed technique to check accuracy of our models. Until now, accuracy of model was not known, we were not having any idea how well our model works. We have explained very comprehensively how to evaluate performance of any of our machine learning model. There are 7 lectures in this section and for sample, we have checked the accuracy of our iris dataset machine learning model (which we trained in previous lecture), which is amazingly 97%.In sixth and last section we have discussed another widely used machine learning algorithm (logistic regression). Using logistic regression we have trained a model to predict whether a patient is suffering from diabetes or not. Within 8 lectures we have created our model which predicts diabetes in a patient with accuracy of almost 79%. In seventh section we have assembled all codes of this course in a single place where you can easily access code of any section of this course.How much time you need to complete this course? Take this course slow and steady. If you give one hour a day, you can complete this course within 20-30 days. When you start this course, make sure that you are consistent with your learning. Time per day      |      time to complete this course0.5 - 1 hour                20 - 35 days1 - 2 hours                 15 - 25 days2 - 3 hours                 7 - 15 daysSpending more than 2 hours is not suggested if this is beginning of your machine learning track. There are 52 lectures in this lecture containing more almost 20 video lectures. We hope you will find this course very helpful in your learning. Suggestion: Practice all codes of this course in your own compiler along with lectures.

Overview

Section 1: Starting up

Lecture 1 Welcome

Lecture 2 What is machine learning

Lecture 3 How machine learns

Lecture 4 Two types of Machine learning

Lecture 5 Intro to ML

Lecture 6 Terms we will be using

Section 2: Supervised machine learning

Lecture 7 Exploring the dataset

Lecture 8 Understanding data for machine learning

Lecture 9 Video explanation of understanding data for machine learning

Lecture 10 Two types of supervised machine learning

Lecture 11 Explaining Classification and Regression problems

Lecture 12 Building our machine learning KNN model

Lecture 13 Building our model part 2

Lecture 14 Video for upper details, loading and understanding our dataset

Lecture 15 Building our model part 3

Lecture 16 separating features and labels

Lecture 17 Building our model part 4

Lecture 18 Building Knn model video detail

Lecture 19 Predictions at once

Lecture 20 How our model works

Lecture 21 how KNN models works, video explanation

Lecture 22 KNN model working for professor Ahmad, understanding concept

Lecture 23 Training model on more than 1 neighbor

Lecture 24 How knn works with more than one neighbors

Lecture 25 All in once

Lecture 26 Any Drawback of nearest neighbors classifier?

Lecture 27 K in Kneighbors classifier

Section 3: Linear Regression, Supervised machine learning

Lecture 28 What is linear regression and why use this

Lecture 29 Making our linear regression model

Lecture 30 Linear regression model for professor Ahmad

Lecture 31 Testing our new model

Lecture 32 Line of best fit

Lecture 33 How linear regression works, explaining line of best fit

Lecture 34 How linear regression works, part 2

Lecture 35 Line of best fit part 2

Section 4: KNeighbors Classifier

Lecture 36 When to use nearest neighbors classifier

Lecture 37 Iris dataset, classification problem

Lecture 38 How to make model on iris data, video lecture

Section 5: Performance of our model

Lecture 39 How well our model works

Lecture 40 Splitting data

Lecture 41 Accuracy of Iris ML model

Lecture 42 Understanding the technique for model accuracy

Lecture 43 Explanation of calculating accuracy on iris dataset model

Section 6: another machine learning algorithm: Logistic Regression

Lecture 44 What Logistic regeession

Lecture 45 Video explanation of logistic regression

Lecture 46 working with diabetes dataset

Lecture 47 Making logistic regression model

Lecture 48 Making logistic regression model part 2

Lecture 49 Creating diabetes logistic regression model

Lecture 50 Accuracy of our model

Lecture 51 Video explanation of accuracy of diabetes model

Section 7: All codes

Lecture 52 All codes here

Machine learning and data science enthusiast (startups),Problem-Solvers, who want to apply machine learning algorithms to solve real world challenges,One who want to excel his data science skills and machine learning algorithms,One who want to build robust models to deal with real life problems