Supervised Machine learning in Python by Harman Waheed

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.
Who this course is for:
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
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