Machine Learning from the scratch using Python

Name: Machine Learning from the scratch using Python
Rating: 4.1 (90 reviews)

Machines are now learning, why aren't you?

Created bySaheb Singh Chaddha

Last updated 4/2020

English

What you'll learn

Great knowledge of Machine Learning and Deep Learning Algorithms.
Work on real case studies
5 projects to work on which can be easily put up on resume for better placements.
Build your own ML Algorithm, Models and Predictions.
Hands-on Numpy, Panda, Matplotlib, etc and many more

Course content

2 sections • 35 lectures • 15h 2m total length

Introduction to Python in Data Science8:41
Arithmetic Functions10:02
Defining, Storing Variables and Datatypes8:41
Working with Data Types9:35
PRACTICE8:36
Introduction to Lists7:25
SLICING6:42
Accessing List Values7:24
Sub-setting Lists7:13
Advanced List Operations7:08
Built in Functions 1.15:55
Built in Functions 1.26:29
Function Arguments 1.18:28
Function Arguments 1.28:35
Introduction to String Methods 1.17:00
Introduction to String Methods 1.27:09
Importing Python Packages9:06
Introduction to String Methods 1.37:49
Subsetting and Comparing Arrays 1.18:26
Introduction to NumPy Arrays7:22
Subsetting and Comparing Arrays 1.27:56

Introduction to AI15:24
This is an introduction to AI, where you will be getting an idea what will be taught, subsets, scope, real time examples of AI.
Introduction to ML( Supervised and Unsupervised Learning)37:45
In this video I will be introducing to ML and what exactly is smart algorithm and how to install and run Anaconda to work with Python.
ML : KNN ( Lp Norms)1:00:54
You will be learning KNN ( K- Nearest Neighbor ) and various Lp Norms like L1 and L2 Norms.
ML : KNN ( Euclidean and Manhattan Distance)57:57
ML : KNN ( Minkowski, Hamming and Cosine Distance )59:31
ML : Over and Under Fitting( Cross Validation and K-Fold CV )1:00:10
Project 1 : Creating the First Model using KNN and finding the Accuracy.55:32
Find the attached Data set to work on the Project!
ML : Linear Regression53:11
Project 2 : based on SIMPLE LINEAR REGRESSION35:48
Find the attached Data set to work on the Project!
Project 3 : based on MULTIPLE LINEAR REGRESSION45:50
Find the attached Data set to work on the Project!
ML : HYPOTHESIS TESTING ( Statistics Fundamentals )1:02:01
ML : Decision Tree with Gini Index51:11
ML : Decision Tree with Information Gain40:12
Project 4 : CASE STUDY based on DECISION TREE1:40:55
Ask me for the python file of the solved dataset on sahebsinghchaddha@gmail.com

Requirements

Basic Python, Mathematics and Statistics

Description

This course is for those who want to step into Artificial Intelligence domain, specially into Machine Learning, though I will be covering Deep Learning in deep as well.

This is a basic course for beginners, just if you can get basic knowledge of Python that would be great and helpful to you to grasp things quickly.

There are 4-5 Projects on real data set which will be very helpful to start your career in this domain, Right now if you don't see the project, don't panic, it might have gone old so I've put it down for modifications.

Enjoy and Good Luck.

Who this course is for:

Beginner or Stepping into AI, ML, DL domain with 4-5 Projects on real data set.

Machine Learning from the scratch using Python

What you'll learn

Explore related topics

Course content

Python for Machine Learning21 lectures • 2hr 46min

Machine Learning14 lectures • 12hr 16min

Requirements

Description

Who this course is for: