Learn to build a healthcare solution using machine learning

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Asad Ali

5:17:17

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1 - Introduction

1 - Introduction.mp4

02:24

2 - How to take the course.html

3 - Python recap.html

4 - Jupyter Notebooks and Google Collaboratory.html

5 - Github and Codespaces.html

2 - Basics of Machine Learning

6 - Intro to this section.html

7 - Machine Learning Basics 1.mp4

05:58

8 - Machine Learning Basics 2.mp4

02:21

9 - Machine Learning Stages 1.mp4

05:00

10 - Machine Learning Stages 2.mp4

04:23

11 - Intro to tech stack for the project 1.mp4

10:08

12 - Intro to tech stack for the project 2.mp4

10:07

3 - Project Preliminary Data Analysis

13 - What we will cover in this section.html

14 - Machine Learning for health care sector.html

15 - Business case for our project.mp4

05:11

16 - Data Ingestion 1.mp4

10:39

17 - More on AWS resources.html

18 - Data Ingestion 2.mp4

05:09

19 - Data Ingestion 3.mp4

03:06

20 - Data Summary and data types.mp4

07:51

21 - Working with dates.mp4

02:11

22 - Modelling the target variable.mp4

16:06

23 - Categorical Encoding.mp4

08:43

24 - Univariate analysis.mp4

09:22

25 - Handling Missing data.mp4

09:37

26 - Linear Regression Basics 1.mp4

10:17

27 - Linear Regression Basics 2.mp4

06:08

28 - Bivariate and regression analysis.mp4

14:56

4 - Project Advanced Data Analysis

29 - What we will cover in this section.html

30 - KMeans Clustering theoryOptional.html

31 - Kmeans clustering 1.mp4

07:44

32 - Kmeans clustering 2.mp4

09:54

33 - Outlier Analysis.mp4

12:32

34 - Data Leakage.mp4

02:24

5 - Building ML Models

35 - What you will cover in this course.html

36 - Ensemble Methods Basics 1.mp4

11:56

37 - Ensemble Methods Basics 2.mp4

22:58

38 - Decision Tree and Random Forest.mp4

12:02

39 - Evaluating Random Forest Models.mp4

12:32

40 - HyperOpt Optimization of hyper parameters.mp4

03:59

41 - Partial Dependency Plots.mp4

07:27

42 - LightGBM.mp4

04:23

43 - Sklearn Pipeline.mp4

02:45

44 - XGboost.mp4

04:52

45 - Neural Networks.mp4

16:36

6 - Model Deployement

46 - What we will cover in this section.html

47 - Getting started with deployment.mp4

05:05

48 - Writing our production code.mp4

06:34

49 - Writing our FastAPI server.mp4

08:41

50 - Docker Getting started.mp4

06:33

51 - Docker implementation 1.mp4

05:53

52 - Docker implementation 2.mp4

02:50

Description

Learn to use machine learning scikit-learn, Tensorflow, Docker to build a real-world healthcare project in under 6 hours

What You'll Learn?

Learn intuition behind many of real-world algorithms
Learn to use Pandas for Data Analysis
Complex tree based algorithms made easy such as Random Forest, Decision Trees, Xgboost and lightgbm
Learn to use Seaborn for statistical plots
Learn and implement Machine Learning Algorithms using python
Learn to use Matplotlib for Python Plotting
Learn basics of multivariate Regression
Learn to use HyperOpt Machine learning optimization
Learn and master K-Means Clustering
Learn Principle Component Analysis
Learn to use Keras for Tensorflow Models
Learn to evaluate different types of Machine learning models
Learn to use FastAPI
Learn to dockerize your apps using Docker
Learn the basics of deploying your models

Who is this for?

Students and Professionals wanting to learn and advance their machine learning skills and get job-ready skills

More details

Description
Are you interested in applying your data science and machine learning skills to a real-world healthcare challenge? Do you want to learn how to develop accurate models for healthcare projects, such as predicting patient length of stay in hospitals using cutting-edge tech tools and frameworks like pandas, Sklearn, random forest,Â xgboost, TensorFlow, and Docker? Then our "Get Job-Ready with our Practical ML and Data Science Course" is the perfect opportunity for you!
Through this hands-on course, you will gain experience with various tech tools and frameworks, including pandas for data manipulation, sklearn for modeling, xgboost for gradient boosting, TensorFlow for deep learning, and Docker for deployment. You'll also learn how to use FastAPI to create efficient and scalable web applications.
The course is structured in the following way:
Section 1:Â We will cover the Basics of Machine learning and introduction to many of the technologies to be applied in this course
Section 2:Â We will cover establishing our business case for the project, perform data ingestion and data preprocessing Simple and Multiple Linear regression,Â univariate and multivariate analysis
Section 3: We will cover K-means clustering, PCA(Principle component analysis), and outlier/anomaly detection methods
Section 4: We will develop various machine learning models such as decision trees, random forest, xgboost ,lightbgm and deep neural networksÂ
Section 5:Â We will cover the deployment of created models using Docker/FastAPI and will create an efficient and scalable API using FastAPI. You'll also learn how to test and optimize your model to ensure that it meets the performance requirements.
By taking the course,Â you will also have the opportunity to build your portfolio of real-world healthcare projects to showcase your skills to potential employers.
By the end of the course, you'll have gained the confidence and practical skills you need to land your dream job in machine learning and data science and to make a real impact in the healthcare industry. So why wait? Sign up for our "Get Job-Ready with our Practical ML and Data Science Course" today, and start your journey to success!
Who this course is for:
Students and Professionals wanting to learn and advance their machine learning skills and get job-ready skills

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Machine Learning and data science professional engineer with more than ten years of experience developing enterprise business analytical solutions completed several ML/analytics projects while working for major global utilities and energy companies. I have hands-on experience in building a diverse range of analytical and machine learning projects ranging from energy forecasting, predictive maintenance, and fleet optimizations. Similarly, I am experienced in using a vast array of tools from Hadoop, Apache Spark, Tensorflow/Pytorch to React/Javascript.I have completed my master's degree in operations research and am passionate about sharing knowledge with the community.

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