Machine learning for chemical industries to boost profit

• Are you ready to take your machine learning skills to the next level? Look no further than our comprehensive online course, designed to take you from beginner to advanced levels of machine learning expertise. Our course is built from scratch, with a focus on real-life case studies from industry and hands-on projects that tackle real industry problems.

• We know that machine learning can be a complex field, which is why our course covers all major algorithms and techniques. Whether you're looking to improve your regression models, build better classifiers, or dive into deep learning, our course has everything you need to succeed. And with our emphasis on practical, hands-on experience, you'll be able to apply what you learn to real-world scenarios right away.

• But what sets our course apart from the rest? For starters, our focus on real-life case studies means that you'll be learning from the experiences of industry professionals who have already solved complex problems using machine learning. This means that you'll be able to see firsthand how machine learning can be applied to a variety of industries, from chemcal,petrochemcal to petroleum refnery.

• In addition, our hands-on projects are specifically designed to tackle real industry problems, so you'll be able to build your portfolio with projects that have practical applications in the workforce. And with our expert instructors available to answer your questions and provide guidance every step of the way, you'll have all the support you need to succeed in this exciting field.

• So if you're ready to take your machine learning skills to the next level, enroll in our comprehensive online course today. You'll gain the knowledge and practical experience you need to succeed in this high-demand field, and you'll be on your way to building a rewarding career in no time.

• The course was created by a Data Scientist and Machine Learning expert from industry to simplify complex theories, algorithms, and coding libraries.

• The uniqueness of this course is that it helps you develop skills to build machine learning applications for complex industrial problems.

• Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

• With over 1000 worldwide students, this course guides you step-by-step through the world of Machine Learning, improving your understanding and skills.

• You can complete the course in matlab

This course is designed to take you from the basics of machine learning to the advanced level of building machine learning models for real-life problems. Here's a brief overview of what you can expect to learn:

1. Introduction to machine learning: In this section, you'll learn about the types of machine learning, the use of machine learning, and the difference between human learning and machine learning. You'll also gain insight into how machines learn and the difference between AI, machine learning, and deep learning.

2. Overview of different types of machine learning: You'll explore real-life examples of machine learning and the different elements of machine learning.

3. Steps in machine learning: You'll dive into the steps involved in the machine learning process, from data pre-processing to building machine learning models.

4. Data pre-processing: In this section, you'll learn how to detect outliers, handle missing values, and encode data to prepare it for analysis.

5. Overview of regression and model evaluation: You'll learn about different model evaluation matrices, such as MAE, MSE, RMSE, R square, and Adjusted R square, and how to interpret them. You'll also learn about overfitting and underfitting.

6. Case study of Bio reactor modelling: You'll walk through a complete case study of building a machine learning model for bio reactor modelling.

7. Building machine learning models: You'll learn how to import and prepare data, select the model algorithm, run and evaluate the model, and visualize the results to gain insights.

8. Detail of modelling by following algorithm: You'll dive into different modelling algorithms, such as linear regression models, decision trees, support vector machine regression, Gaussian process regression model, kernel approximation models, ensembles of trees, and neural networks.

9. Real-life case study to build soft-sensor for distillation column: You'll explore a real-life case study of building a soft-sensor for a distillation column.

10. Case study to build an ML model of catalytic reactor: You'll learn about another real-life case study of building an ML model for a catalytic reactor.

11. Case study to build an ML model for running plant: You'll explore a case study of building an ML model for a running plant.

12. Modelling by Artificial Neural Network (ANN): You'll gain insight into artificial neural networks, including ANN learning, training, calculation, and advantages and disadvantages. You'll also explore a case study of ANN.

Detail of course:

1. Introduction to machine learning

a. What is machine learning(ML)?

b. Types of machine learning

c. Use of machine learning

d. Difference between human learning and machine learning

e. What is intelligent machine?

f. Compare human intelligence with machine intelligence

g. How machine learns?

h. Difference between AI and machine learning and deep learning

i. Why it is important to learn machine learning?

j. What are the various career opportunities in machine learning?

k. Job market of machine learning with average salary range

2. Overview of different type of machine learning

a. Real Life example of machine learning

b. Elements of machine learning

3. Steps is machine learning

4. Data pre-processing

a. Outlier detection

b. Missing Value

c. Encoding the data

5. Overview of regression and model evaluation

a. Model evaluation matrices, eg. MAE,MSE,RMSE,R square, Adjusted R square

b. Interpretation of these performance matrices

c. Difference between these matrices

d. Overfitting and under fitting

6. Walk through a complete case study of Bio reactor modelling by machine learning algorithm

7. Building machine learning models

a. Overview of regression learner in matlab

b. Steps to build a ML Model

c. Import and Prepare data

d. Select the model algorithm

e. Run and evaluate the model

f. Visualize the results to gain insights

8. Detail of modelling by following algorithm

1. 1. Linear regression models

   2. Regression trees

   3. Support vector machine regression

   4. Gaussian process regression model

   5. Kernel approximation models

   6. Ensembles of trees

   7. Neural Network

9. Real life case study to build soft-sensor for distillation column

10. Case study to build ML model of catalytic reactor

11. Case study to Build ML model for running plant

12. Modelling by Artificial Neural Network (ANN)

a. Introduction of ANN

b. Understanding ANN learning

c. ANN Training

d. ANN Calculation

e. Advantages and Dsiadvantages of ANN

f. Case study of ANN

• Each section is independent, so you can take the whole course or select specific sections that interest you.

• You will gain hands-on practice with real-life case studies and access to matlab code templates for your own projects.

• This course is both fun and exciting, and dives deep into Machine Learning.

• Overall, this course covers everything you need to know to build machine learning models for real-life problems. With hands-on experience and case studies from industry, you'll be well-prepared to pursue a career in machine learning. Enroll now to take the first step towards becoming a machine learning expert!