What you'll learn
Master Machine Learning with matlab
Develop intuition for various Machine Learning models
Make accurate predictions
Conduct powerful analysis
Build robust Machine Learning models
Create added value for businesses
Apply Machine Learning for personal purposes
Choose appropriate Machine Learning models for different types of problems
Build an arsenal of powerful Machine Learning models and learn how to combine them to solve any problem.
Develop skills to solve real life industry problem through machine learning
Requirements
No programming experience needed.
This course will build the skill from scratch
Elementary knowledge of matlab is desirable but not compulsory.
Description
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 healthcare to finance to e-commerce.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 matlabThis 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: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.Overview of different types of machine learning: You'll explore real-life examples of machine learning and the different elements of machine learning.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.Data pre-processing: In this section, you'll learn how to detect outliers, handle missing values, and encode data to prepare it for analysis.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.Case study of Bio reactor modelling: You'll walk through a complete case study of building a machine learning model for bio reactor modelling.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.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.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.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.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.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 learninga. What is machine learning(ML)?b. Types of machine learningc. Use of machine learningd. Difference between human learning and machine learninge. What is intelligent machine?f. Compare human intelligence with machine intelligenceg. How machine learns?h. Difference between AI and machine learning and deep learningi. 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 range2. Overview of different type of machine learninga. Real Life example of machine learningb. Elements of machine learning3. Steps is machine learning4. Data pre-processinga. Outlier detectionb. Missing Valuec. Encoding the data5. Overview of regression and model evaluationa. Model evaluation matrices, eg. MAE,MSE,RMSE,R square, Adjusted R squareb. Interpretation of these performance matricesc. Difference between these matricesd. Overfitting and under fitting6. Walk through a complete case study of Bio reactor modelling by machine learning algorithm7. Building machine learning modelsa. Overview of regression learner in matlabb. Steps to build a ML Modelc. Import and Prepare datad. Select the model algorithme. Run and evaluate the modelf. Visualize the results to gain insights8. Detail of modelling by following algorithmLinear regression modelsRegression treesSupport vector machine regressionGaussian process regression modelKernel approximation modelsEnsembles of treesNeural Network9. Real life case study to build soft-sensor for distillation column10. Case study to build ML model of catalytic reactor11. Case study to Build ML model for running plant12. Modelling by Artificial Neural Network (ANN)a. Introduction of ANNb. Understanding ANN learningc. ANN Trainingd. ANN Calculatione. Advantages and Dsiadvantages of ANNf. Case study of ANNEach 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!
Overview
Section 1: Introduction
Lecture 1 Course Overview
Section 2: Introduction to machine learning
Lecture 2 What is machine learning?
Lecture 3 Type of machine learning
Lecture 4 Real life example of machine learning
Lecture 5 Elements of machine learning
Section 3: Steps in Machine learning
Lecture 6 Steps in machine learning
Section 4: Data Preprocessing
Lecture 7 what is data preprocessing?
Lecture 8 Outlier detection
Lecture 9 Case study of outlier detection
Lecture 10 Missing value
Lecture 11 Encoding the data
Section 5: Overview of model building and model evaluation
Lecture 12 Overview of regression
Lecture 13 Model evaluation and performnace matrices
Lecture 14 When and how to use the evalaution matrices
Lecture 15 Overfitting and Underfitting
Lecture 16 Bias and variance
Section 6: Walk through a complete case study of Bio Reactor model building by ML
Lecture 17 BioReactor Case study part1
Lecture 18 BioReactor Case study part2
Lecture 19 BioReactor Case study part3
Lecture 20 BioReactor Case study part4
Section 7: Building Machine learning models
Lecture 21 Overview of Regression learner app
Lecture 22 Steps to build a ML model
Lecture 23 Import and prepare data
Lecture 24 Select the model algorithm
Lecture 25 Run and evaluate the model
Lecture 26 Visualize the result to gain insights
Section 8: Real life case study to build softsensor for distillation column
Lecture 27 Distillation column casestudy part1
Lecture 28 Distillation column casestudy part2
Lecture 29 Distillation column casestudy part3
Section 9: Case study toi build MLmodel of catalytic reactor
Lecture 30 Casestudy of catalytic reactor part 1
Lecture 31 Casestudy of catalytic reactor part 2
Lecture 32 Casestudy of catalytic reactor part 3
Section 10: Build ML model for running chemical plant
Lecture 33 case study for chemical plant part 1.
Lecture 34 case study for chemical plant part 2
Section 11: Modelling by Artificial Neural Network (ANN)
Lecture 35 Introduction of ANN
Lecture 36 Understanding ANN learning
Lecture 37 ANN Training
Lecture 38 ANN Calculation
Lecture 39 Advantages and Dsiadvantages of ANN
Section 12: Case study of ANN
Lecture 40 Case study Part 1
Lecture 41 Case study Part 2
Lecture 42 Case study Part 3
Individuals interested in Machine Learning,High school students with knowledge in math looking to learn Machine Learning,Intermediate level individuals familiar with classical algorithms like linear and logistic regression, but want to explore different fields of Machine Learning,Non-coders interested in Machine Learning and easy application on datasets,College students pursuing a career in Data Science,Data analysts seeking to advance their Machine Learning skills,Individuals looking to transition into a career as a Data Scientist,Business owners looking to create added value through powerful Machine Learning tools,Experienced engineers (specially chemical engineers) who worked in industry and want to increase profit of their organization with Machine Learning tools


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