Here is the list of the 10 Best Training and Certification courses for Machine Learning
1. Machine Learning training – Offered by Stanford
This course provides a Progressive introduction of concepts of machine learning, data mining, and statistical pattern recognition. Here are the topics included in this very well structured course Topics include: (a) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (b) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (c) Best practices in ML (bias/variance theory; innovation process in machine learning and AI).
| Offered By | Number of users enrolled | Users Rating | Course Duration |
| Stanford | 2,605,890 | 4.9 | 56 Hours |
Level – Intermediate
Skills to learn
- Logistic Regression
- Artificial Neural Network
- Machine Learning (ML) Algorithms
- Machine Learning
Useful for
- Technical Leads
- Machine Learning Engineers
- Chief Technology Officers (CTOs)
- Software Engineers
- Risk Managers
Review Highlights–
- Around 40% of students started a new career after completing this course
- Around 38% got a tangible career benefit after this course
Conclusion
This course instructed by Andrew Ng is extremely helpful and understandable for engineers and researchers in the CS field. It covers a wide range of topic. Quite helpful for professionals as well as for engineering students.
2. Machine Learning Specialization– Offered by the University of Washington
This ML specialization course is a pack of four courses which include Machine Learning Foundations: A Case Study Approach, Machine Learning: Regression, Machine Learning: Classification, and Machine Learning: Clustering & Retrieval. Through the series of practical case studies, one will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. Will also get the skill to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
| Offered By | Number of users enrolled | Users Rating | Course Duration |
| University of Washington | 69,028 | 4.65 | 8 Months |
Level – Intermediate
Skills to learn
- Data Clustering Algorithms
- Machine Learning
- Classification Algorithms
- Decision Tree
Useful for
- Data Scientists
- Machine Learning Engineers
- Data Analysts
- Risk Managers
- Data Engineers
Review Highlights–
After finishing every course and completing the hands-on project, one is entitled to get a Certificate that can be used for prospective employers and professional networks.
Conclusion – Although this course is for a longer duration, it gives liberty to set a flexible deadline. The course is beautifully designed for the aspirants of data science.
3. Introduction to Artificial Intelligence (AI) – Offered by IBM
Although this course does not require any programming or computer science expertise, it is designed that one will be exposed to various issues and concerns surrounding. One will be able to learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks.
| Offered By | Number of users enrolled | Users Rating | Course Duration |
| IBM | 10,181 | 4.7 | 4 Weeks |
Level – Intermediate
Skills to learn
- Data Science
- Deep Learning
- Artificial Intelligence (AI)
- Jobs
- Machine Learning
Useful for
- This course is for everyone. No prior background in computer science or programming is necessary.
Review Highlights–
This is one of the interesting course designed for the foundation of AI and to explore applications of AI.
Conclusion – The course is perfect for fresher. It provides a clear understanding of ML, AI, Deep Learning and mathematically advanced concepts.
4. Machine Learning Certification Training using Python – Offered by Edureka
This course teaches the concepts of Statistics, Time Series and different classes of machine learning algorithms using Python. It helps in gaining expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. In this certification course, one will be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR, etc.
| Offered By | Number of users enrolled | Users Rating | Course Duration |
| Edureka | 6000+ | 4.57 | 6 Weeks |
Level – Intermediate with knowledge of development experience with Python and Fundamentals of Data Analysis practised over any of the data analysis tools
Skills to learn
- Insight into the ‘Roles’ played by an ML Engineer
- Data pre-processing,
- Dimensional reduction
- Model evaluation
- Automate data analysis using python
- Tools and techniques for predictive modelling
- Machine Learning algorithms and their implementation
- Validation of ML algorithms
- Time Series and its related concepts
Useful for
- Aspirants of ‘Machine Learning Engineer’
- Analytics Managers
- Business Analysts interested in understanding Machine Learning (ML) Techniques
- Information Architects aspiring to get expertise in Predictive Analytics
- Python professionals willing to design automatic predictive models
Review Highlights-
Apart from being a high-quality instructor and course content, Edureka’s support also is very professional and fast responsive. Lifetime access to the Learning Management System along with 24×7 online support.
Conclusion – Machine Learning Certification Training using Python is one of the top-rated courses. The best part is live instruction, with playback. Edureka courses provide lifetime access for the courses so that any time one can start playback session and learn in case of any problem in any of the courses. Towards the end of the course, one will be working on a project. Based on the project completion, Edureka certifies the student as a Data Scientist with proficiency in Python. This course is one of the right choices for the aspirants of data science.
5. Intro to Machine Learning – Offered by Udacity in collaboration with Kaggel and AWS
This program is intended for students with experience in Python, who have not yet studied Machine Learning topics. A course designed for step by step learning starting data cleaning and supervised models and then exploring deep and unsupervised learning. At each step, one gets practical experience by applying skills to code exercises and projects.
| Offered By | Users Rating | Course Duration |
| Udacity, Kaggel & AWSEdureka | 4.85 | 3 Months |
Level – Beginners with prerequisite knowledge of Python programming knowledge and basic knowledge of probability and statistics.
Skills to learn
This program emphasizes practical coding skills that demonstrate your ability to apply machine learning techniques to a variety of business and research tasks. It is designed for people who are new to machine learning and want to build foundational skills in machine learning algorithms and techniques to either advance within their current field or position themselves to learn more advanced skills for a career transition. In this program, one will apply machine learning techniques to a variety of real-world tasks, such as customer segmentation and image classification.
Useful for
This course is best suited for the students and aspirants who have the intermediate Python programming knowledge and basic knowledge of probability and statistics and are looking to build their career in machine learning and data science
Review Highlights- This program is designed to teach foundational machine learning skills that data scientists and machine learning engineers use.
Conclusion – The Intro to Machine Learning Nanodegree program is comprised of content and curriculum to support three (3) projects. Each project will be reviewed by the Udacity reviewer network. Feedback will be provided.
6. Machine Learning with Python: A Practical Introduction – Offered by IBM
This course teaches the basics of Machine Learning using Python. One will learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. The learner will explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error and Random Forests.
| Offered By | Course Duration |
| IBM | 5 Weeks |
Level – Intermediate with knowledge of Python basics for data science.
Skills to learn
- Supervised vs Unsupervised Machine Learning
- How Statistical Modeling relates to Machine Learning, and how to do a comparison of each.
- Different ways machine learning affects society
Useful for
- The course is designed to build the fundaments of Machine learning for those who have decide to pursue career in ML or Data science.
Review Highlights- This course is free to learn without a Professional Certificate
Conclusion – With the hands-on of real life examples of Machine Learning one will be able to transform theoretical knowledge into practical skill. Recommended for beginners.
7. Machine Learning Engineer Nanodegree – Offered by Udacity in collaboration with Kaggel and AWS
This course teaches the advanced machine learning techniques and algorithms and how to package and deploy models to a production environment
| Offered By | Users Rating | Course Duration |
| Udacity, Kaggel & AWSEdureka | 4.9 | 3 Months |
Level – Intermediate to Advanced – This course is designed for the students who already have the knowledge of intermediate python and machine learning algorithms.
Skills to learn
- Production-level code and practice object-oriented programming
- Build a Python package
- Deploy machine learning models to a production environment using Amazon SageMaker
- Build and deploy a deep learning model that predicts the sentiment
- Creating a simple web app that uses a deployed model and accepts user input.
- Machine learning challenge and propose a possible solution
- Advanced machine learning skills to define similarity metrics between two text documents
Useful for
The course is very well suited for the aspirants who are interested in building and deploying a machine learning product or application. Students in the Machine Learning Engineer Nanodegree program will learn about machine learning algorithms and crucial deployment techniques and will be equipped to fill roles at companies seeking machine learning engineers and specialists.
Review Highlights-
- Real-world projects from industry experts
- 1-on-1 technical mentor
- Personal career coach & career services
Conclusion – This course is designed to teach the advanced skills one need to become a machine learning engineer. One will learn how to create an end-to-end machine learning product and deploy machine learning models to a production environment, such as a web application, and evaluate and update that model according to performance metrics.
8. Machine Learning training Certification Course – Offered by Simplilearn
This Machine Learning online course provides insights into the vital roles played by machine learning engineers and data scientists. The course is a hands-on, code-driven training that will help you apply your machine learning knowledge. You will work on 4 projects that encompass 25+ ancillary exercises and 17 machine learning algorithms.
| Offered By | Course Duration |
| Simplilearn | 44 hours of instructor-led training with certification |
Level – Intermediate with prerequisite fundamentals of Python programming and understanding of the basics of statistics and mathematics
Skills to learn
- Concepts of supervised and unsupervised learning, recommendation engine, and time series modelling
- Principles, algorithms, and applications of machine learning
- Statistical and heuristic aspects of machine learning
- Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
- Validate machine learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques
- Comprehend theoretical concepts and how they relate to the practical aspects of machine learning
Useful for
- Developers aspiring to be data scientists or machine learning engineers
- Analytics managers who are leading a team of analysts
- Business analysts who want to understand data science techniques
- Information architects who want to gain expertise in machine learning algorithms
- Analytics professionals who want to work in machine learning or artificial intelligence
- Graduates looking to build a career in data science and machine learning
- Experienced professionals who would like to harness machine learning in their fields to get more insights
Review Highlights-
- A hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
- Upon successful completion of this course, Simplilearn will provide an industry-recognized course completion certificate which has lifelong validity.
Conclusion – Upon completion of this course, one will be able to uncover the hidden value in data using Python programming for futuristic inference. One can work with real-time data across multiple domains including e-commerce, automotive, social media and more and will learn how to develop machine learning algorithms using concepts of regression, classification, time series modelling and much more. This is a very well organized course ideal for the aspirants of data scientists or machine learning engineers.
9. Machine Learning A-Z™: Hands-On Python & R In Data Science – Offered by Udemy
This course teaches to create Machine Learning Algorithms in Python and R.
| Offered By | Number of users enrolled | Course Duration |
| Udemy | 458,624 | 41 hours |
Level – Beginners
Skills to learn
- Master Machine Learning on Python & R
- Have a great intuition of many Machine Learning models
- Accurate predictions
- Powerful analysis
- Make robust Machine Learning models
- Create strong added value to your business
- Use Machine Learning for personal purpose
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
- Build an army of powerful Machine Learning models and learn how to combine them to solve any problem
Useful for
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any students in college who want to start a career in Data Science.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Review Highlights
Highly recommended if you want to get some hands on experience of Implementing Machine Learning algorithms with proper understanding.
Conclusion – This course is fun and exciting, but at the same time gives a dive deep into Machine Learning. The course has been designed by two professional Data Scientists who have shared their knowledge to help students to learn complex theory, algorithms and coding libraries in a simple way.
10. Python for Data Science and Machine Learning Bootcamp – Training course by Udemy
This course teaches how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more.
| Offered By | Number of users enrolled | Users Rating | Course Duration |
| Udemy | 242672 | 4.5 | 22 hours |
Level – Beginners with high school level programming experience
Skills to learn
- Python for Data Science and Machine Learning
- Use Spark for Big Data Analysis
- Implement Machine Learning Algorithms
- Learn to use NumPy, Pandas. Matplotlib, Seaborn, Plotly, SciKit-Learn
- K-Means Clustering
- Logistic Regression, Linear Regression
- Random Forest and Decision Trees
- Natural Language Processing and Spam Filters
- Neural Networks and Support Vector Machines
Useful for
For the people having interest in the field of data science with at least some programming experience
Review Highlights-
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
Conclusion – Excellent course that teaches the fundamental skills with Python and Data Science! Recommend for people trying to learn python without any prior experience.
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