Real World Data Science and Machine Learning Projects
3.6 (79 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,487 students enrolled

Real World Data Science and Machine Learning Projects

Apply Machine Learning Algorithms and Build 8 real world machine learning projects in Python
3.6 (79 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,487 students enrolled
Last updated 3/2020
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This course includes
  • 4 hours on-demand video
  • 5 articles
  • 13 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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
Requirements
  • Should Know Basics of Machine Learning
  • Jupyter notebook
  • Should Know about Machine Learning Libraries
  • A passion to learn data science
Description

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

Who this course is for:
  • Beginner in machine learning
  • Want to build real world machine learning projects
Course content
Expand all 51 lectures 04:02:27
+ Introduction
3 lectures 02:37
Udemy Review
00:56
Install Jupyter Notebook
00:33
+ Project-1 Predicting presence of Heart Diseases using Machine Learning
5 lectures 34:31
Data Preprocessing
06:07
Data Preprocessing 2
04:27
Model Building- 1
07:00
Model Building- 2
11:28
+ Project-2 Malaria detection using deep learning model (CNN)
6 lectures 27:54
Importing Libraries and Data
06:47
Initializing CNN model
05:13
Data Generation
02:42
Prepare Train and Test set
03:26
Fitting and Prediction
03:18
Testing on Random Images
06:28
+ Project-3 Build a Music Recommendation system
8 lectures 51:19
Importing Libraries and Data
04:13
Data Visualization 1
08:34
Merge Data
04:44
Missing values
08:02
Data Visualization 2
06:42
Preparing Data
06:08
Model Building
10:12
+ Project-4 Predicting Prices of Bitcoin with Machine Learning
4 lectures 16:33
Importing Libraries and Data
03:26
Data Preprocessing
05:07
Train and Test set
02:31
Model Building
05:29
+ Project-5 Breast Cancer Prediction using Machine Learning
4 lectures 19:08
Importing Libraries and Data
03:23
Data Preprocessing
04:06
Data Visualization
03:11
Model Building
08:28
+ Project-6 Human activity recognition using smartphones
8 lectures 38:47
Importing Data
05:06
Data Preprocessing
05:16
1st Model NN
04:36
Logistic Regression
04:50
PCA and Feature Scaling
03:40
Random Forest & KNN
04:41
Decision Tree & Grid SearchCV
08:16
+ Project-7 Time Series Prediction with LSTM Recurrent Neural Networks
4 lectures 21:37
Importing Libraries and Data
04:28
Data Preprocessing 1
03:51
Data Preprocessing 2
05:10
Model Building
08:08
+ Project-8 Predicting Diabetes With Machine Learning
5 lectures 27:28
Importing Libraries and Data
03:15
Data Preprocessing
06:00
Data Visualization
05:32
Model Building 1
04:13
Model Building 2
08:28
+ ##BOUNUS- Useful ML Code Snippets
4 lectures 02:32
How To Find Optimal Parameters Using RandomizedSearchCV?
00:55
How to find optimal parameters using GridSearchCV?
00:23
How to optimise number of trees in XGBoost ?
00:42
How to compare sklearn classification algorithms in Python ?
00:31