Machine Learning Foundation With Practical Approaches

Free Download Machine Learning Foundation With Practical Approaches
Published 8/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.94 GB | Duration: 10h 7m
This course have been created keeping in mind to deliver the foundation of ML to students, working professionals.

What you’ll learn
One of the best slides and learning material from scratch for Learners.
Learn very basics to pro level in machine learning.
Learn the practical application which they can use with ML.
Identify what strategy they can use to solve a given ML problem.
Drive a given ML projects and have great understanding about end to end ML approaches.
Requirements
Simple basic Python programming.
Eager to learn and explore.
Description
Hey there, I am a professional ML engineer, working for a big US retail client, and worked on many complex problems with their solutions. This course is all about my experience and what I think is important for students to learn. Please join me and I hope you enjoy this course, the same way I did making this course for you.In this course, we will be discussing ML algorithms along with detailed examples. We will also be discussing the final end project that will comprise building a real-life very common application. This course not only targets very new people like students but also targets experienced professionals looking to increase their knowledge of ML.The only expectation is you should know some basic Python programming and be able to install Python libraries like sklearn, pandas, scipy, etc, and Jupyter Notebook in which we have coded our solution.We have divided this course into several sections each section will go first into detail in a given presentation which is free to download and use for your purpose and then that course fully goes into the demo which will increase your understanding of how to implement that in the Python library. Once you figure out the pattern of the course we have developed it will be fairly easy that you can explore and search for your own solution and thought process to develop your project.It’s an amazing journey that we can have, Let’s learn together and build a better world with Machine learning.
Overview
Section 1: Introduction
Lecture 1 ML-Introduction
Lecture 2 Setup our Environment
Section 2: Statistics and mathematics
Lecture 3 Introduction
Lecture 4 Statistics Concepts
Lecture 5 Statistics hands on
Lecture 6 Mathematics Concepts in ML
Lecture 7 Mathematics Hands on Demo
Section 3: Data Preprocessing
Lecture 8 Data Preprocessing Concepts
Lecture 9 Data Preprocessing Hand on
Section 4: Feature Engineering
Lecture 10 Feature Engineering Concepts
Lecture 11 Feature Engineering Hands on
Section 5: Regression
Lecture 12 Introduction
Lecture 13 Algorithm Discussions Concepts
Lecture 14 Algorithm Discussions Hands On
Lecture 15 Regression Evaluation Technique concepts
Lecture 16 Regression Evaluation Technique Hands On
Section 6: Classification Algorithms
Lecture 17 Introduction
Lecture 18 Algorithm discussion Concepts
Lecture 19 Algorithm Discussion Hands on
Lecture 20 Classification Evaluation Technique Concepts
Lecture 21 Classification Evaluation Technique Hands on
Section 7: Unsupervised Learning
Lecture 22 Introduction
Lecture 23 Algorithm Discussion Concepts
Lecture 24 Algorithm Discussion Hands on
Section 8: Time series Modelling concepts
Lecture 25 Introduction
Lecture 26 Algorithm Discussion Concepts
Lecture 27 Algorithm Discussion Hands on
Section 9: Ensemble Learning
Lecture 28 Introduction
Lecture 29 Algorithm Discussions and Concepts
Lecture 30 Algorithm Discussion hands on
College Students.,Experience and Working professional who wants to kickstart a great ML journey.,Beginner ML and Data science enthusiast curious to learn end to end course.,Students who want to enhance their ML knowledge.
Homepage

www.udemy.com/course/machine-learning-foundation-with-practical-approaches/