Machine Learning Projects A-Z - Kaggle and Real World Pro

Machine Learning Projects A-Z : Kaggle and Real World Pro, available at $19.99, has an average rating of 4, with 59 lectures, based on 108 reviews, and has 1470 subscribers.

You will learn about Solve Competitive level Problems like Kaggle Get your Profile Ready for Interviews This course is ideal for individuals who are Anyone who wants to bulid his carreer in Data Science / Machine Learning It is particularly useful for Anyone who wants to bulid his carreer in Data Science / Machine Learning.

Enroll now: Machine Learning Projects A-Z : Kaggle and Real World Pro

Summary

Title: Machine Learning Projects A-Z : Kaggle and Real World Pro

Price: $19.99

Average Rating: 4

Number of Lectures: 59

Number of Published Lectures: 59

Number of Curriculum Items: 59

Number of Published Curriculum Objects: 59

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Solve Competitive level Problems like Kaggle

Get your Profile Ready for Interviews

Who Should Attend

Anyone who wants to bulid his carreer in Data Science / Machine Learning

Target Audiences

Anyone who wants to bulid his carreer in Data Science / Machine Learning

Want to join Kaggle Competition?

Want to Experience how Real Data Scientists Solve Problems in Real World?

Then this is a right course for you.

This course has been designed by IIT professionals who have mastered in Mathematics and Data Science. We will walk you step-by-step how to solve Machine Learning Projects and With every tutorial you will develop new skills which in turn improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is meant for experienced IT Project Managers who want to understand how to manage Machine Learning projects, what are the specific challenges they will face, and what are some best practices to help them successfully deliver business value.

Course Curriculum

Chapter 1: Introduction to the course

Lecture 1: Introduction

Chapter 2: Project 1 : Kaggle

Lecture 1: Introduction to the Problem Statement

Lecture 2: Playing With The Data

Lecture 3: Translating the Problem In Machine Learning World

Lecture 4: Dealing with Text Data

Lecture 5: Train, Test And Cross Validation Split

Lecture 6: Understanding Evaluation Matrix: Log Loss

Lecture 7: Building A Worst Model

Lecture 8: Evaluating Worst ML Model

Lecture 9: First Categorical column analysis

Lecture 10: Response encoding and one hot encoder

Lecture 11: Laplace Smoothing and Calibrated classifier

Lecture 12: Significance of first categorical column

Lecture 13: Second Categorical column

Lecture 14: Third Categorical column

Lecture 15: Data pre-processing before building machine learning model

Lecture 16: Building Machine Learning model :part1

Lecture 17: Building Machine Learning model :part2

Lecture 18: Building Machine Learning model :part3

Lecture 19: Building Machine Learning model :part4

Lecture 20: Building Machine Learning model :part5

Lecture 21: Building Machine Learning model :part6

Chapter 3: Project 2: Real world Problem

Lecture 1: Part1

Lecture 2: Part2

Lecture 3: Part3

Lecture 4: Part4

Lecture 5: Part5

Chapter 4: Project 3: Real world Project

Lecture 1: Part1

Lecture 2: Part2

Lecture 3: Part3

Lecture 4: Part4

Chapter 5: Project 4: Real World Project

Lecture 1: Part1

Lecture 2: Part2

Lecture 3: Part3

Lecture 4: Part4

Lecture 5: Part5

Chapter 6: Project 5 : Kaggle

Lecture 1: Part1

Lecture 2: Part2

Lecture 3: Part3

Lecture 4: Part4

Lecture 5: Part5

Lecture 6: Part6

Lecture 7: Part7

Lecture 8: Part8

Lecture 9: Part9

Lecture 10: Part10

Lecture 11: Part11

Lecture 12: Part12

Lecture 13: Part13

Lecture 14: Part14

Lecture 15: Part15

Chapter 7: Project 6: Kaggle

Lecture 1: Part1

Lecture 2: Part2

Lecture 3: Part3

Lecture 4: Part4

Lecture 5: Part5

Lecture 6: Part6

Lecture 7: Part7

Lecture 8: Part8

Instructors

Geekshub Pvt Ltd
BigData and Analytics

Rating Distribution

1 stars: 6 votes

2 stars: 4 votes

3 stars: 20 votes

4 stars: 43 votes

5 stars: 35 votes

Frequently Asked Questions

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