6 Best + Free Applied Machine Learning Courses & Classes [DECEMBER 2020]

Best Applied Machine Learning course tutorial class certification training online

Machine Learning is one of the most demanding skills in the IT Industry. It encourages individuals to bring innovations to the world. So, if you are willing to learn the math underlying the machine learning, then check out this list of Tutorials, Courses, Training, Classes, and Certification programs that are available online for 2020. Our experts have tried to include every valuable source in the list that provides free or paid material to learn machine learning. Whether you’re a beginner willing to start your journey in the machine learning field or an intermediate eager to improve your skills, you can find a relevant course for you in this list. Have a look at our Best Data Science Courses. 

 

6 Best + Free Applied Machine Learning Courses & Classes [DECEMBER 2020]

1. Applied Machine Learning by Columbia Engineering Executive Education (Emeritus)

Created by the professional instructors of Columbia Engineering University, this course focuses on teaching you a wide range of techniques for both supervised and unsupervised machine learning approaches with the help of Python programming. You will begin with an understanding and working knowledge of Python by completing the Python for Data Science course and then move on to learn the core concepts of machine learning. Joining this course will help you cover a variety of machine learning topics, such as Regression, Foundational classification algorithms, Clustering methods, Sequential data models, and more. After submitting the final assignment of the course, you will get a digitally signed certificate of completion. Check our list of Best Python Data Science Courses.

 

Key USPs –

– An ideal course for individuals who are willing to implement, lead a machine learning project, or incorporate machine learning capability in their software application.

– Get introduced to data science concepts like working with data types, writing functions in Python, advanced functions, data cleaning, linear algebra, and more.

– Included with more than 200 faculty video lectures, 40+ quizzes, moderate discussion boards, Q&A session with instructors, and much more.

– Work with multiple assignments and application projects included with the course to help you test your knowledge and skills gained in the course.

 

Duration: 5 months, 8-10 hours/week

Rating: 4.5 out of 5

You can Sign up Here

 

2. Applied Machine Learning in Python by University of Michigan (Coursera)

This course will work as an introductory guide to help you learn the techniques and methods of applied machine learning so that you can implement it in your software applications. It will begin with a discussion on how machine learning is different than descriptive statistics while introducing you to the scikit toolkit. After absorbing the basics, you will learn the core concepts of applied machine learning like the Dimensionality of data, Data clustering, Supervised and Unsupervised approaches, etc. The course is created by the best professors of the University of Michigan, who have been training individuals in machine learning for over a decade.

 

Key USPs –

– Get introduced to the key concepts, tasks, and workflows in machine learning, python tools used in machine learning, examples of machine learning, and more.

– Dive deeper into the wide variety of supervised learning methods for both classification and regression while learning the connection between model complexity and generalization performance.

– Cover the evaluation and model selection methods that can be used to understand and optimize the performance of machine learning models.

– Be able to identify the techniques that you need to apply for a particular dataset and need.

 

Duration: 34 hours

Rating: 4.6 out of 5

You can Sign up Here

 

Review: This is an excellent course. The programming exercises can be solved only when you get the basics right. Else, you will need to revisit the course material. Also, the forums are pretty interactive – PS.

 

3. Applied Machine learning Algorithms (Corporate Finance Institute)

Folks who want to learn how machine learning algorithms can be used to build finance-based applications can take help from this course. Designed by the expert faculty of the Corporate Finance Institute, this course will provide a unique understanding of applied machine learning for finance professionals. You will learn how to build investor classifiers, identify over-fit regression models, and solve real problems in the world of finance with machine learning algorithms. After finishing this curriculum, you will be able to recognize opportunities for solving problems with machine learning in your immediate environment while determining the best machine learning algorithms for any situation. You may want to check our compilation of Best Time Series Analysis Courses. 

 

Key USPs –

– A practical course designed for professionals working in sales and trading, investment banking, capital markets, asset management, and treasury management.

– Learn how to compare various regularized regression algorithms and decision-tree ensemble algorithms while explaining the confusion matric and its relation to the ROC Curve.

– Explore various real case studies from investment banking and capital markets applications that are used for advising Fortune 500 companies.

– Be able to construct training data sets, testing data sets, and model pipeline, as well as perform advanced data cleaning, exploration, and visualization.

 

Duration: Self-paced

Rating: 4.4 out of 5

You can Sign up Here

 

Review: This course is really helpful with my ongoing corporate assignments; the instructions and materials are really helpful and easy to understand. Great Job by CFI again! – Ashutosh Kaithwar.

 

4. Introduction to Applied Machine Learning by Alberta Machine Learning Institute (Coursera)

Ideally designed for professionals, this course will teach you how to apply machine learning to data analysis and automation. Whether you’re working in medicine, finance, engineering, or any other domain, this course will introduce you to common problems and data preparation in a machine learning project. You will learn how to interpret a business need into a machine learning problem with the help of some applied examples, and then move on to understand the definitions and components of MLPL. The course is developed by Anna Koop, who has years of experience in working with machine learning.

 

Key USPs –

– Learn to define a machine learning problem with unique approaches, how to survey available data resources to identify potential ML applications.

– Know about some misconceptions of ML and identify various components essential to a machine learning business solution with this knowledge.

– Walkthrough some of the best-applied examples of ML to understand what makes a well-defined question for your QuAM session.

– Gain knowledge of data acquisition and understand the various sources of training data while knowing the ethical issues that could occur during the process.

 

Duration: 6 hours

Rating: 4.7 out of 5

You can Sign up Here

 

Review: Very comprehensive course on applied machine learning. The most interesting information in this course is the business needs for ML and its requirement to have a good QuAM. – AA.

 

5. Applied Machine Learning (Applied AI Course)

It is 150+ hours of applied machine learning course that is focused on industry standards with simplified content to help individuals gain a solid understanding of machine learning algorithms. Enrolling in this learning path will help you learn some of the core ideas in machine learning, data science, and AI so that they can solve a real-world problem quickly by deploying AI solutions. It is created by experienced instructors who have ensured that there is a proper balance between the theory and practice methods of the course. After the course completion, you will be able to deploy ML algorithms to your organization’s problems.

 

Key USPs –

– Get introduced to the fundamentals of programming, foundations of natural language processing, data science, machine learning, and more.

– Learn about feature engineering, product ionization and deployment of ML models, data mining, neural networks, Computer vision, and deep learning.

– Learn from a personal mentor after 50% course completion, who will help you build a specific portfolio, resume, and prepare you for the interview process.

– Get access to expert guidance, 15+ real-world case studies, 30+ machine learning and deep learning algorithms, free video tutorials, and much more.

 

Duration: Self-paced

Rating: 4.6 out of 5

You can Sign up Here

 

Review: Mathematical building blocks for machine learning were explained in a simple and enjoyable way. – Sristi Bhadani.

 

6. Applied Machine Learning – Beginner to Professional (Analytics Vidhya)

Created by skilled professionals of Analytics Vidhya, this course will provide you with all the tools and techniques that an individual requires to apply machine learning to solve business problems. In this curriculum, you will learn the fundamentals of machine learning, building machine learning models, and deploying those machine learning modules in your organization. You will also get a rich understanding of how machine learning and data science are disrupting multiple industries today. The course is equipped with numerous video sessions, discussion forums, graded quizzes, and other downloadable resources. At the end of the curriculum, you will get the opportunity to work with six real-world projects from the industry to apply your knowledge.

 

Key USPs –

– Learn about linear, logistic regression, decision tree and random forest algorithms that help build machine learning models.

– Gain a solid understanding of how to solve classification and regression problems in machine learning, and how to ensemble modeling techniques like boosting, bagging, and Kernel tricks.

– Know about dimensionality reduction techniques and be able to evaluate your machine learning models and improve them via feature engineering.

– Be able to work with various types of data for machine learning problems like tabular, text, unstructured, etc.

 

Duration: 6-8 weeks, 8-10 hours/week

Rating: 4.5 out of 5

You can Sign up Here