10 Best Machine Learning Certification for 2020

Posted in Machine Learning, Certification

 

Ref. https://hackr.io/blog/machine-learning-certifications

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With Machine Learning being a popular field of AI, the demand for ML engineers is exponentially increasing as companies want to implement and make ML a pivot feature in their products. The profession is much in demand and is making in the list of top jobs.

Top 10 Machine Learning Certification

Let us see some good ranked Machine Learning Certification courses to help you boost your career.

1. Professional Certificate Program in Machine Learning and Artificial Intelligence

The course is highly recommended for professionals and undergraduates to shape their careers. The course ensures businesses and individuals to have an education and necessary training to be successful in the AI-powered future. The certificate equips with the best practices and actionable knowledge needed to put the organization at the forefront of the AI revolution. The MIT faculty experts expose participants to the latest breakthroughs in cutting-edge technologies, research, and other best practices used for building advance AI-systems. The program provides the foundation of knowledge that can be put to immediate use to help people and organizations advance cognitive technology.

Registration: May 2020
Fee: each course costs between $2500-$5500
Course Duration: Varies
Mode of Teaching: Online
Pre-requisites (if-any): Bachelor’s degree in computer science, statistics, physics or electrical engineering.

 

Key Benefits

• Personal training from the faculty and leading industry practitioners.
• Learn skills essential concepts and skills needed to develop practical AI systems.
• Discusses the challenges posed by AI in the workplace.
• Apply industry-relevant, cutting-edge, knowledge in machine learning and AI.
• Network with an experienced group of peers from around the globe.

You can signup here.

2. Machine Learning with TensorFlow on Google Cloud Platform Specialization

Specialization comprises of 5 courses and promises to take you from an overview of the importance of Machine Learning to lectures about building ML models. The program consists of introductory-level lessons and covers what machine learning is capable of and why is it so popular, followed by classes that focus on Tensorflow, an open-source machine learning framework. These sets of lectures aim to create, train, deploy ML models, solve numerical problems, and much more. There are also numerous hands-on opportunities to enhance the accuracy of ML using the various features available on the Google Cloud Platform.

Registration: Every 2 months on Coursera
Fee: [Financial Aid Available]
Course Duration: 9 weeks (Self-paced)
Mode of Teaching: Online
Pre-requisites (if-any): Computer science or engineering background.

Key Benefits

• The course covers everything from basics like machine learning concepts to what kind of problem it can solve.
• Teaches to create machine learning models that scale in TensorFlow, and how to scale out the training of those models.
• Teaches to integrate the right combination of parameters that harvest accurate, generalized models and knowledge of the theory.
• Get hands-on labs available with the Google cloud platform and enhance your skills.
• Opportunity to share your information directly with Google and Publicis to be considered for open hiring opportunities.
• Earn a Specialization Certificate to share with your professional network and potential employers.

You can signup here.

3. Machine Learning Stanford Online

The course provides a broad introduction to statistical pattern recognition and machine learning. Differentiates between supervised and unsupervised learning as well as learning theory, reinforcement learning, and control. Explores recent applications of machine learning and design and develop algorithms for machines.

Registration: August -September
Fee: $5040
Course Duration: 3 months
Mode of Teaching: Online
Pre-requisites (if-any): Computer science or engineering background.

Key Benefits

• Basics concepts of machine learning
• Generative learning algorithms
• Evaluating and debugging learning algorithms
• Bias/variance tradeoff and VC dimension
• Value and policy iteration
• Q-learning and value function approximation

You can signup here.

4. Professional Certificate in Foundations Of Data Science

The course provides a new lens to explore issues and problems. It teaches us to combine data with Python programming skills to explore encountered problems in any field of study or a future job. The program also helps aspiring data scientists by teaching them how to analyze a diverse array of real data sets, including geographic data, economic data, and social networks. The course also teaches inference, which helps to quantify uncertainty and measures the accuracy of your estimates. Finally, all the knowledge is put together and to teach prediction with the help of machine learning. The program aims to make data science accessible to everyone.

Registration: 2-4 months on edX
Fee: $267
Course Duration: 4months(self-paced)
Mode of Teaching: Online
Pre-requisites (if-any): This course is specifically designed for beginners who do not have any computer or statistics background and no programming experience

Key Benefits

You learn :

• To draw robust conclusions based on incomplete information by critical thinking.
• Python 3 programming language for analyzing and visualizing
• data and other computational thinking and skills
• To make predictions based on machine learning.
• To communicate and interpret data and results using a vast array of real-world examples.

You can signup here.

5. Certification of Professional Achievement in Data Sciences

Multiple courses such as algorithms for data science, machine learning for data science, probability, and statistics, exploratory data analysis are covered in this course. This course is suited for candidates having prior knowledge in statistics, linear algebra, probability, & calculus.programming. The certification prepares students to expand their career prospects or change career paths by developing foundational data science skills.

Registration: Deadline February 15 for Fall
Fee: $24,216
Course Duration: 12 months
Mode of Teaching: Online and Campus
Pre-requisites (if-any):

• Undergraduate degree
• Prior quantitative coursework (calculus, linear algebra, etc.)
• Prior introductory to computer programming coursework

Key Benefits

• Learn the basics of computational thinking, using Python.
• Learn to use inferential thinking to make conclusions about unknowns based on data in random samples.
• Learn to use machine learning, with a focus on regression and classification, to automatically identify patterns in the data and make better predictions.

You can signup here.

6. eCornell Machine Learning Certificate

Cornell’s Machine Learning certificate program equips to implement machine learning algorithms using Python. Using a combination of math and intuition, students learn to frame machine learning problems and construct a mental model to understand the approach of data scientists to these problems programmatically. Implementation of concepts such as k-nearest neighbors, naive Bayes, regression trees, and others are explored with a variety of machine learning algorithms.

The program allows implementing algorithms on live data while practicing debugging and improving models through approaches such as support vector machines and as ensemble methods. Finally, the coursework explores the inner workings of neural networks and how to construct and adapt neural networks for various types of data.

This program uses Python and the NumPy library for code exercises and projects. Projects are completed using Jupyter Notebooks.

Registration: throughout the year
Fee: $3,600 or $565/month
Course Duration: 3.5 months
Mode of Teaching: Online
Pre-requisites (if-any): Python

Key Benefits

• Redefine problems using machine learning terminology and concepts.
• Develop a face recognition system using algorithms.
• Implement the Naive Bayes algorithm and estimate probabilities distribution from data.
• Create an email spam filter by implementing a linear classifier
• Improve the prediction accuracy of an algorithm by using a bias-variance trade-off.
• Use an effective hyperparameter search to select a well-suited machine learning model and implement a machine learning setup from start to finish.
• Train a neural network.

You can signup here.

7. Certificate in Machine learning

This three-course certificate program examines all aspects of machine learning. Concepts like probability and statistical methods that are at the core of machine learning algorithms are taught in this course. It also practices ways to apply these techniques, using open-source tools along with your developing judgment and intuition to address actual business needs and real-world challenges

 

Registration: throughout the year
Fee: $4,548
Course Duration: Varies
Mode of Teaching: Online
Pre-requisites (if-any):

Key Benefits

• Concepts of probability, statistical analyses, mathematical modeling, and optimization techniques
• Supervised and unsupervised learning models for tasks such as forecasting, predicting and outlier detection
• Advanced machine learning applications, including recommendation systems and natural language processing
• Deep learning concepts and applications
• How to identify, source and prepare raw data for analysis and modeling

You can signup here.

8. Harvard University Machine Learning

This course teaches principal component analysis, popular machine learning algorithms, and regularization by building a recommendation system for movies.

The course teaches about training data and how to use a set of data to discover potentially predictive relationships. By building the movie recommendation system, students learn how to train algorithms using training data to predict the outcome for future datasets. The course also teaches overtraining and techniques to avoid it, e.g., cross-validation.

Registration: throughout the year on edX
Fee: Free
Course Duration: 8 weeks
Mode of Teaching: Online
Pre-requisites (if-any): Python

Key Benefits

• The basics of machine learning
• How to perform cross-validation to avoid overtraining
• Several popular machine learning algorithms
• How to build a recommendation system
• What is regularization and why it is useful?

You can signup here.

9. Machine Learning with Python

This course covers the basics of machine learning using well-known programming language, Python. The course reviews two main components: First, learning about the purpose of Machine Learning and where it applies to the real world.

Second, it provides a general overview of Machine Learning topics such as supervised vs. unsupervised learning, model evaluation, and Machine Learning algorithms.

Registration: throughout the year on edX
Fee: Free
Course Duration: 8 weeks
Mode of Teaching: Online
Pre-requisites (if-any): Python

Key Benefits

Learn new skills such as regression, classification, clustering, and SciPy

Opportunity to add new projects that you can add to your portfolio, including predicting economic trends, cancer detection, predicting customer churn, recommendation engines, and many more.

 

A certificate in machine learning to prove your competency

You can signup here.

10. Machine Learning at Udacity

The course consists of two modules to discuss various types of machine learning.

The first module covers Supervised Learning, a machine learning task that trains for your email to filter spam, your phone to recognize your voice, and for computers to learn a bunch of other cool stuff.

The second module teaches about Unsupervised Learning. Ever wonder how Amazon knows what you want to buy before you do? Or how Netflix can predict what movies you'll like? This section answers such questions.

Finally, it answers, can we program machines to learn like humans? This Reinforcement Learning section teaches algorithms for designing self-learning agents like us!

Registration: throughout the year on Udacity
Fee: Free
Course Duration: 4 months
Mode of Teaching: Online
Pre-requisites (if-any):

You can signup here.

Key Benefits

Supervised Learning

• Machine Learning is the ROX
• Decision Trees
• Regression and Classification
• Neural Networks
• Instance-Based Learning
• Ensemble B&B
• Kernel Methods and Support Vector Machines (SVM)s
• Computational Learning Theory
• VC Dimensions
• Bayesian Learning
• Bayesian Inference

Unsupervised Learning

• Randomized optimization
• Clustering
• Feature Selection
• Feature Transformation
• Information Theory

Reinforcement Learning

• Markov Decision Processes
• Reinforcement Learning
• Game Theory

I hope this recommended list of certification courses were helpful for you. Were they? Do you have more courses to share? Comment Below !!

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