Udemy

Platform

English

Language

IT Certification

Category

Real World Data Science and Machine Learning Projects

Apply Machine Learning Algorithms and Build 8 real world machine learning projects in Python

4.30 (15 reviews)

Real World Data Science and Machine Learning Projects

Students

4 hours

Content

Aug 2021

Last Update
Regular Price


What you will learn

Train machine learning algorithms to detect Heart Diesease.

Build a Music Recommendation system.

Train machine learning algorithms to detect Breast Cancer

Train machine learning algorithms to predict Diabetes

Automated Malaria detection using deep learning models like CNN

Bitcoin price prediction using machine learning

Time Series Prediction with LSTM Recurrent Neural Networks

Artificial intelligence, Data science, Machine learning, Deep learning projects


Description

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to self-learn and improve over time without being explicitly programmed. In short, machine learning algorithms are able to detect and learn from patterns in data and make their own predictions.

In traditional programming, someone writes a series of instructions so that a computer can transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action.

Machine learning, on the other hand, is an automated process that enables machines to solve problems and take actions based on past observations.

Basically, the machine learning process includes these stages:

  1. Feed a machine learning algorithm examples of input data and a series of expected tags for that input.

  2. The input data is transformed into text vectors, an array of numbers that represent different data features.

  3. Algorithms learn to associate feature vectors with tags based on manually tagged samples, and automatically makes predictions when processing unseen data.

While artificial intelligence and machine learning are often used interchangeably, they are two different concepts. AI is the broader concept – machines making decisions, learning new skills, and solving problems in a similar way to humans – whereas machine learning is a subset of AI that enables intelligent systems to autonomously learn new things from data.

In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques. We are going to build 8 projects from scratch using real world dataset, here’s a sample of the projects we will be working on:

  • Build a Music Recommendation system.

  • Human activity recognition using smartphones

  • Time Series Prediction with LSTM Recurrent Neural Networks

  • Predicting presence of Heart Diseases using Machine Learning

  • Automated malaria detection using deep learning models like CNN

  • Predicting Prices of Bitcoin with Machine Learning

  • Breast Cancer Prediction using Machine Learning

  • Predicting Diabetes With Machine Learning Techniques


Content

Introduction

Introduction

Udemy Review

Install Jupyter

Project-1 Predicting presence of Heart Diseases using Machine Learning

Importing Libraries and Data

Data Preprocessing 1

Data Visualization 2

Model Building 1

Model Building 2

Download The Project Code

Project-2 Malaria detection using deep learning model (CNN)

Importing Libraries and Data

Initializing CNN model

Data Generation

Prepare Train and Test set

Fitting and Prediction

Testing on Random Images

Download The Project Code

Project-3 Build a Music Recommendation system

Introduction

Importing Libraries and Data

Data Visualization 1

Merge data

Missing values

Data visualization 2

Preparing data

Building model

Download The Project Code'

Project-4 Predicting Prices of Bitcoin with Machine Learning

Importing Libraries and Data

Data Preprocessing

Train and Test set

Model Building

Download The Project Code

Project-5 Breast Cancer Prediction using Machine Learning

Importing Libraries and Data

Data Preprocessing

Data Visualization

Model Building

Download The Project Code

Project-6 Human activity recognition using smartphones

Introduction

Importing Data

Data Preprocessing

1st Model NN

Logistic Regression

PCA and Feature Scaling

Random Forest & KNN

Decision Tree & Grid SearchCV

Download The Project Code

Project-7 Time Series Prediction with LSTM Recurrent Neural Networks

Importing Libraries and Data

Data Preprocessing 1

Data Preprocessing 2

Model Building

Download The Project Code

Project-8 Predicting Diabetes With Machine Learning

Importing Libraries and Data

Data Preprocessing

Data Visualization

Model Building 1

Model Building 2

Download The Project Code

BONUS##

How To Find Optimal Parameters Using RandomizedSearchCV ?

How to find optimal parameters using GridSearchCV?

How to optimise number of trees in XGBoost ?

How to compare sklearn classification algorithms in Python ?


Reviews

A
Atharva17 April 2021

Lecturer's presentation is nice and clear. We can practice the skill set we learned from this class a lot from the assignment after class. It is vert practical! You can follow the hint to do the assignment step by step even though you are not feel comfortable to finish it from scratch.

V
Vanyambadi17 April 2021

I finished the course and I have to say it was beyond awesome. one of the very few courses that I have actually finished!

V
Vaibhav17 April 2021

loved the professional behavior of instructor well designed course slow in start but keep up speed with time.


3972358

Udemy ID

4/10/2021

Course created date

4/16/2021

Course Indexed date
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