Machine Learning Real World projects in Python
4.7 /5- Offered byUDEMY
Machine Learning Real World projects in Python at UDEMY Overview
Duration | 11 hours |
Total fee | ₹455 |
Mode of learning | Online |
Credential | Certificate |
Machine Learning Real World projects in Python at UDEMY Highlights
- Earn a certificate from Udemy
- Get a 30 day money back guarantee
- Learn from 3 articles and case studies
- Students will get full lifetime access of the course
Machine Learning Real World projects in Python at UDEMY Course details
- For data Scientists who want to apply their knowledge on Real World Case Studies
- For machine Learning Enthusiasts who look to add more projects to their Portfolio
- Machine Learning Engineers earn on average $164,000 - become Job Ready ML Engineer with this course
- Go from zero to hero in Entire Pipeline of Machine learning from Data Collection to building a Machine Learning Model
- Solve any problem in your business, job or in real-time with powerful Machine Learning algorithms
- Mathematics behind All Machine Learning algos ( Linear Regression , logistic , Decision Tree , Ensemble algos , KNN , Naive Bayes & many more
- Various Feature selection Techniques & how to apply it in Real-World
- How to Approach a problem in Real-world
- The course covers a number of different machine learning algorithms such as Regression and Classification algorithms
- From there students will learn how to incorporate these algorithms into actual projects so they can see how they work in action
- In addition to quizzes that students will find at the end of each section, the course also includes a 3 brand new projects that can help them experience the power of Machine Learning using real-world examples
Machine Learning Real World projects in Python at UDEMY Curriculum
Introduction
How to follow this course-Must Watch
Installation of Anaconda Navigator
03:46
Quick Summary of Jupyter Notebook
Life Cycle of Machine Learning Project
Introduction to Business Problem & Dataset
Datasets & Resources
Analysing Demand Of hotels
Select Important features using Machine learning
How to extract Derived features from data
How to Handle Outliers
Applying Techniques of Feature Importance
Intuition behind Logistic Regression --part 1
Intuition behind Logistic Regression --part 2
Idea Behind Cross Validation- Part 1
Idea Behind Cross Validation- Part 2
Applying logistic regression on data & cross-validate it
Intuition Behind Decision Tree- Part 1
Intuition Behind Decision Tree- Part 2
Prepare your data for Analysis & Modelling
How to clean your data
Analysing Distributions of your data
How to check co-relation in data
How to Automate your Analysis
Perform Exploratory Data Analysis on data..
How to come across with missing values in data
Clean your missing values using Random Value Imputation
Introduction to Business Problem & Dataset
Datasets & Resources
Understand your data
How to extract Derived features from data
Perform Data Pre-processing
Handle Categorical Data & Feature Encoding
How to Perform Label Encoding on dataset
Outliers Detection in Data
Select best Features using Feature Selection Technique
Machine Learning Real World projects in Python at UDEMY Entry Requirements
- Prerequisites: Basic knowledge of Python programming is recommended
- Not mentioned
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