Machine Learning In-Depth (With Python)

Name: Machine Learning In-Depth (With Python)
Rating: 4.2 (22 reviews)

Created byHarish Masand

Last updated 11/2023

English

What you'll learn

Machine Learning In-depth,
Covers Supervised Learning (Regression and Classification)
Covers Unsupervised Learning (Dimensionality Reduction and Clustering)
This is pre requisite for Deep Learning, Reinforcement Learning, NLP, and other AI courses
Completing this course will also make you ready for most interview questions for Machine Learning

Course content

1 section • 24 lectures • 46h 1m total length

Introduction to Data Science Career Path1:33
Day 1 - Introduction to ML2:22:11
Day 2A - ML End to End (Day 1 of 2)1:12:19
Day 2B - ML End to End (Day 1 of 2)1:47:06
Day 3 - ML End to End (Day 2 od 2)1:52:17
Day 4 - ML - Linear Regression2:31:13
Day 5 - ML - Linear Regression Continue2:10:33
Day 6 - ML - Linear Regression Continue2:34:31
Day 7 - ML - Linear Regression Continue2:31:40
Day 8 - ML - Linear Regression Practical2:10:19
Day 9 - ML - Introduction to Classification2:44:28
Day 10 - ML - Introduction to Classification2:30:27
Day 11 - ML - Introduction of Logistics Regression2:30:13
Day 12 - ML - Logistics Regression Practical2:06:36
Day 13 - ML - Introduction of Decision Tree2:28:55
Day 14 - ML - Decision Tree (Cont) and Hands On2:28:47
Day 15A - ML - Random Forest and Hands on1:52:35
Day 15B - ML - Random Forest and Hands on29:18
Note: Data set is shared in Decision Tree section. Same dataset is used here as well
Day 16 - ML - Support Vector Machines2:15:06
Day 17 - ML - Support Vector Machines Hands On50:12
Note: Data set is shared in Decision Tree section. Same dataset is used here as well
Day 18 - ML - Principal Component Analysis3:22:57
Day 19 - ML - Principal Component Analysis Hands On10:22
Day 20 - ML - Clustering2:25:03
Day 21 - ML - Clustering Hands On33:07

Requirements

Data Analysis In-Depth Course by myself on udemy

Description

Machine Learning In-Depth (With Python)

1. What will students learn in your course?

Machine Learning In-depth, Covers Introduction, Supervised Learning including regression and classification, Unsupervised Learning including dimensionality reduction and clustering.

Very few courses covers basics and algorithm in detail, and here you will find clear and simple explanation and practical implementation

Completing this course will also make you ready for most interview questions for Data Science /Machine Learning Role related to Supervised Learning including regression and classification, Unsupervised Learning including dimensionality reduction and clustering.

This is Pre-requisite for Deep Learning, Reinforcement Learning, NLP, and other AI courses

2. What are the requirements or prerequisites for taking your course?

Good to do my "Data Analysis In-Depth (With Python)" course on Udemy

3. Who is this course for?

People looking to advance their career in Data Science and Machine Learning roles

Already working in Data Science/ ML Ops Engineering roles and want to clear the concepts

Want to make base strong before moving to Deep Learning, Reinforcement Learning, NLP, LLM, Generative AI and other AI courses

Currently working as Full Stack developer and want to transition to Machine Learning Engineer roles

4. Is this course in depth and will make industry ready?

Absolutely yes, it will make you ready to creack Machine Learning Interviews and solve ML problems. This will also lay strong foundation for Deep Learning, Reinforcement Learning, etc

5. I am new to IT/Data Science, Will i understand?

Absolutely yes, it is taught in most simplest way for every one to understand

Who this course is for:

People looking to advance their career in Data Science and Data Analytics
Already working in Data Science/ Data Analyst Roles and want to clear the concepts
Want to make base strong before moving to Machine Learning, Deep Learning, Reinforcement Learning, NLP, and other AI courses
Currently working as Data Analyst and want to progress to Data Science Roles