Machine Learning Practical Course- Build 30 Projects

Machine Learning Practical Course: Build 30 Projects, available at $54.99, has an average rating of 3.95, with 184 lectures, based on 45 reviews, and has 2122 subscribers.

You will learn about Real life case studies and projects to understand how things are done in the real world Implement Machine Learning Algorithms Learn to create machine learning models Learn best practices for real-world data sets. This course is ideal for individuals who are Beginners in machine learning It is particularly useful for Beginners in machine learning.

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Summary

Title: Machine Learning Practical Course: Build 30 Projects

Price: $54.99

Average Rating: 3.95

Number of Lectures: 184

Number of Published Lectures: 173

Number of Curriculum Items: 184

Number of Published Curriculum Objects: 173

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Real life case studies and projects to understand how things are done in the real world

Implement Machine Learning Algorithms

Learn to create machine learning models

Learn best practices for real-world data sets.

Who Should Attend

Beginners in machine learning

Target Audiences

Beginners in machine learning

Machine learning has inserted itself into the fiber of our everyday lives – even without us noticing. Machine learning algorithms have been powering the world around us, and this includes product recommendations at Walmart, fraud detection at various top-notch financial institutions, surge pricing at Uber, as well as content used by LinkedIn, Facebook, Instagram, and Twitter on users’ feeds, and these are just a few examples, grounded directly in the daily lives we live.

This being said, it goes without saying that the future is already here – and machine learning plays a significant role in the way our contemporary imagination visualises it. Mark Cuban, for instance, has said: “Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.”

Machine learning makes a mockery of anything that can be called “important” – both at a financial as well as a global scale. If you are looking to take your career to another level, Machine Learning can do that for you. If you are looking to involve yourself in something that will make you part of something that is global as well as contemporary relevance, Machine Learning can do that for you as well.

Machine learning covers significant ground in various verticals – including image recognition, medicine, cyber security, facial recognition, and more. As an increasing amount of businesses are realising that business intelligence is profoundly impacted by machine learning, and thus are choosing to invest in it.

Netflix, to take just one example, announced a prize worth $1 million to the first person who could sharpen its ML algorithm by increasing its accuracy by 10%. This is sureshot evidence that even a slight enhancement in ML algorithms is immensely profitable for the companies that use them, and thus, so are the people behind them. And with ML, you can be one of them!

The best machine learning engineers these days are paid as much as immensely popular sports personalities! And that’s no exaggeration! According to Glassdoor, the average machine learning engineer salary is 8 lakhs per annum – and that’s just at the starting of one’s career! An experienced machine learning engineer takes home anywhere between 15 to 23 lakhs per annum.

Course Curriculum

Chapter 1: Introduction To The Course

Lecture 1: Introduction To The Course

Lecture 2: Course Outline

Chapter 2: Project-1: Image Caption Bot

Lecture 1: Introduction Importing Libraries and dataset

Lecture 2: data cleaning

Lecture 3: data preprocessing-

Lecture 4: data preparation

Lecture 5: Training the model

Lecture 6: Download The Project Files

Chapter 3: Project-2: Costa Rican household poverty prediction

Lecture 1: Importing Libraries and dataset

Lecture 2: Data preprocessing and feature engineering

Lecture 3: Creating models

Lecture 4: Download The Project Files

Chapter 4: Project-3: Stroke prediction problem

Lecture 1: Importing Libraries and dataset

Lecture 2: data preprocessing

Lecture 3: creating models

Lecture 4: PCA

Lecture 5: Download The Project Files

Chapter 5: Project-4: Car price prediction

Lecture 1: Importing Libraries and dataset

Lecture 2: understanding the data

Lecture 3: creating and hypertunning model

Lecture 4: Download The Project Files

Chapter 6: Project-5: Bigmart sales prediction

Lecture 1: Importing Libraries and dataset

Lecture 2: understanding the data

Lecture 3: EDA

Lecture 4: Creating models

Lecture 5: Hypertuning

Lecture 6: Download The Project Files

Chapter 7: Project-6: Loan Prediction Analysis

Lecture 1: Importing Libraries and dataset

Lecture 2: data preprocessing, visualization

Lecture 3: Creating models

Lecture 4: hypertuning models

Lecture 5: Download The Project Files

Chapter 8: Project-7: Predicting employee attrition

Lecture 1: Importing Libraries and dataset

Lecture 2: data preprocessing and visualization

Lecture 3: Feature selection and model building

Lecture 4: Hypertuning

Lecture 5: Download The Project Files

Chapter 9: Project-8: Predicting Hotel Booking

Lecture 1: Importing Libraries and dataset

Lecture 2: data preprocessing, EDA

Lecture 3: Feature engineering, model building

Lecture 4: Download The Project Files

Chapter 10: Project-9: Apparent temperature prediction

Lecture 1: Importing lib data

Lecture 2: dataprocessing

Lecture 3: Model building part1

Lecture 4: Model building part2

Lecture 5: Download The Project Files

Chapter 11: Project-10: Consumer Complaint classification

Lecture 1: Importing

Lecture 2: Data preprocessing

Lecture 3: Model building

Lecture 4: Hyperparameter tuning

Lecture 5: Download The Project Files

Chapter 12: Project-11: Live Sketch Project

Lecture 1: Introduction to case study

Lecture 2: Understanding the code

Lecture 3: Write function to capture video

Lecture 4: completing the function

Lecture 5: testing the code

Lecture 6: Download The Project Files

Chapter 13: Project-12: Traditional Dance Project-13: Leaf disease detector Project-14: Car

Lecture 1: Introduction

Lecture 2: Introductio To Colab

Lecture 3: Importing the dataset

Lecture 4: splitting the data

Lecture 5: creating the model

Lecture 6: complile the model

Lecture 7: Train the model

Lecture 8: Download The Project Files

Chapter 14: Project-13: Leaf disease detector

Lecture 1: Introduction to Leaf Disease Detector

Lecture 2: Importing Libraries and dataset Leaf Disease Code

Lecture 3: Creating inception layer

Lecture 4: Data processing and creating model

Lecture 5: Compiling the model

Lecture 6: Training the model

Lecture 7: Download The Project Files

Chapter 15: Project-14: Car Brand Identification

Lecture 1: Car Brand Detector

Lecture 2: Importing the data Car Brand Detector

Lecture 3: Creating the model

Lecture 4: Compile the model

Lecture 5: Train the model

Lecture 6: Download The Project Files

Chapter 16: Project-15: Tweet Analysis Project

Lecture 1: Introduction

Lecture 2: Importing libraries

Lecture 3: Understanding the data

Lecture 4: Splitting the data

Lecture 5: Data Preprocessing

Lecture 6: Creating the model

Instructors

The Machine Learning
Machine Learning Engineer

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 1 votes

4 stars: 4 votes

5 stars: 37 votes

Frequently Asked Questions

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