Machine Learning Real World Projects In Python (updated 2/2023)

Build a Portfolio/Resume of Machine Learning projects_case studies in Python & get a job of Data Scientist/ ML Engineer

What you'll learn

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..

Case studies

Requirements

Basic knowledge of Python programming is recommended.

Description

This is the first course that gives hands-on Machine Learning Projects using Python..Student Testimonials:Excellent Course and amazing work @shan singh sir I am glad to be your student one small suggestion is try to show the deployment phase like showing our end project for others using flask, streamlit, gradio etc It will help lot of us to learn about the UI and deployment as well. - Veluturi Sunil TagoreVery clear and understood explanation using case study. It was very clear showing the beauty of the function and the visualization when he used according to the case study. Thanks - SadikUAnother great course! Real world data, various data manipulations techniques, practical visualizations and insights, useful ways of automation. I've learnt a lot in a short period of time. Solid 5 stars! Thanks - Sebastian SuminskiThe course is really good and practical. The explanation right to the point. Definitely recommended for who learned the theory and want to do hands- on - Deepthi kiran Chebrolu Machine Learning is one of the hottest technology field in the world right now! This field is exploding with opportunities and career prospects. Machine Learning techniques are widely used in several sectors now a days such as banking, healthcare, finance, education transportation and technology.This course covers several technique in a practical manner, the projects include coding sessions as well as Algorithm Intuition:So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because in a practical life, machine learning seems to be complex and tough,thats why we’ve designed a course to help break it down into real world use-cases that are easier to understand.1.Task #1 @Predicting the Hotel booking  : Predict Whether Hotel booking  is going to cancel or not3.Task #2 @Predict Whether Person has a Chronic Disease or not: Develop a Machine learning Model that predicts whether person has Chronic kidney disease or not2.Task #3 @Predict the Prices of Flight: Predict the prices of Flght using Regression & Ensemble Algorithms..The course covers a number of different machine learning algorithms such as Regression and Classification algorithms. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 3 brand new projects that can help you experience the power of Machine Learning using real-world examples!

Overview

Section 1: Intro to this course

Lecture 1 Introduction & Course Benefits

Lecture 2 Utilize QnA of the course ( Golden Oppurtunity ) !

Lecture 3 How to follow this course-Must Watch

Lecture 4 Installation of Anaconda Navigator

Lecture 5 Quick Summary of Jupyter Notebook

Section 2: Introduction to Life-Cycle of Machine Learning Project

Lecture 6 Life Cycle of Machine Learning Project

Section 3: Project 1–>> Predict the cancellation of Hotel Booking

Lecture 7 Introduction to Business Problem & Dataset

Lecture 8 Datasets & Resources

Lecture 9 How to read data

Lecture 10 Lets Perform data cleaning..

Lecture 11 Analysing Demand Of hotels

Lecture 12 Analysing Prices of Hotels across year

Lecture 13 Analysing Demand Of hotels

Lecture 14 Lets Analyse which month has highest avg. daily rate ?

Lecture 15 Lets perform Advance Data Analysis ..

Lecture 16 How to create useful features for Machine Learning model ..

Lecture 17 how to apply Feature encoding on Categorical data .

Lecture 18 How to Handle Outliers .

Lecture 19 Select important Features using Co-relation & univariate analysis

Lecture 20 Applying Techniques of Feature Importance .

Lecture 21 Intuition behind Logistic Regression –part 1

Lecture 22 Intuition behind Logistic Regression –part 2

Lecture 23 Building Machine Learning model .

Lecture 24 Idea Behind Cross Validation- Part 1

Lecture 25 Idea Behind Cross Validation- Part 2

Lecture 26 How to cross-validate model .

Lecture 27 Intuition Behind Decision Tree- Part 1

Lecture 28 Intuition Behind Decision Tree- Part 2

Lecture 29 Intuition Behind Decision Tree- Part 3

Lecture 30 Intuition Behind Decision Tree- Part 4

Lecture 31 Intuition Behind Decision Tree- Part 5

Lecture 32 Intuition Behind Decision Tree- Part 6

Lecture 33 Intuition Behind Random Forest Part-1

Lecture 34 Intuition Behind Random Forest Part-2

Lecture 35 Intuition Behind KNN- Part 1

Lecture 36 Intuition Behind KNN- Part 2

Lecture 37 Intuition Behind KNN- Part 3

Lecture 38 Intuition Behind KNN- Part 4

Lecture 39 Intuition Behind Naive Bayes-Part 1

Lecture 40 Intuition Behind Naive Bayes-Part 2

Lecture 41 Applying Multiple algorithms on data

Section 4: Project 2–>> Predict status of Chronic kidney disease (Health care use-case )

Lecture 42 Datasets & Resources

Lecture 43 Prepare your data for Analysis & Modelling

Lecture 44 How to clean your data

Lecture 45 Analysing Distributions of your data

Lecture 46 How to check co-relation in data

Lecture 47 How to Automate your Analysis

Lecture 48 Perform Exploratory Data Analysis on data..

Lecture 49 How to come across with missing values in data

Lecture 50 Clean your missing values using Random Value Imputation

Lecture 51 Applying feature Encoding on data

Lecture 52 How to Select best features for your model

Lecture 53 How to handle Imbalance data in Machine Learning !

Lecture 54 Building a Cross-validated Model & checking its accuracy

Section 5: Project 3–>> Predict Prices of Airline Tickets

Lecture 55 Introduction to Business Problem & Dataset

Lecture 56 Datasets & Resources

Lecture 57 How to read data !

Lecture 58 Lets Perform data-preprocessing & extract derived Features .

Lecture 59 Perform Data Cleaning & Feature Engineering

Lecture 60 Lets Perform Data Analysis

Lecture 61 How to pre-process Duration Feature.

Lecture 62 Analyse whether Duration impacts Price or not ?

Lecture 63 Lets Perform Bi-variate Analysis !

Lecture 64 Handle Categorical Data & Applying One-hot Encoding on data .

Lecture 65 Applying target guided encoding on data..

Lecture 66 Outliers Detection in Data

Lecture 67 Select best Features using Feature Selection Technique

Lecture 68 Apply Random Forest Algorithm on Data ..

Lecture 69 Intuition Behind Linear Regression- Part 1

Lecture 70 Intuition Behind Linear Regression- Part 2

Lecture 71 Intuition Behind Linear Regression- Part 3

Lecture 72 How to automate Machine Learning pipeline

Lecture 73 How to Hypertune your model

Section 6: Bonus Section

Lecture 74 Bonus Lecture

Data Scientists who want to apply their knowledge on Real World Case Studies,Machine Learning Enthusiasts who look to add more projects to their Portfolio