Are you looking for guidelines from the Best Machine Learning Course Online, Tutorial, Training, and Certification? Here are the best machine learning courses that are highly recommended for you.
Machine learning is being used in a wide range of applications today. Machine learning (ML) is a category of an algorithm that allows software applications.
To become more accurate in predicting outcomes without being explicitly programmed, Machine learning is one of the most well-known instances.
38 Best Machine Learning Course Online, Tutorial, Training, and Certification
The basic premise of machine learning is to build algorithms. That can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.
Machine learning also plays a very important role in artificial intelligence. Nowadays the importance of machine learning has increased in business operation.
The ultimate list of online courses will guide you through everything you need to know.
1. Machine learning
When people hear the name of machine learning there are different types of thoughts cross their mind. They are confused about which code is needed to have the proper knowledge of machine learning.
Here is the perfect machine learning course for the beginner. This will be a great course to get started on the machine learning journey. This machine learning tutorial is offered by Stanford University. This is the best course for anyone who has no previous knowledge in machine learning.
About 40% of students get a new career and 37% got a tangible career benefit after completing this course. The rating of this tutorial is 4.9 which is good enough to understand the importance of it.
This machine learning course is a 100% online course with fixable deadlines. It takes approximately 55 hours to complete. This tutorial course also provides financial aid for the students who cannot afford to pay the tuition fees to do this course.
Andrew Ng is the expert instructor here. Form him students can learn everything that they need to know and will understand the practical side of machine learning.
From this machine learning course, you will learn about the skills of logistics regression, artificial neural network, machine learning algorithms, and machine learning.
In this machine learning Introductory course, you will learn the basics of machine learning and you will understand the core algorithms. You will also become confident enough to dive deeper into the field and apply machine learning to solve different problems with some superficial understanding.
In this machine learning course, you will learn how to give trained data to a program and get better results for the data complex problem. Data timing is a very important term that you learn from this training. By doing this machine learning course, you will gather the idea of statistical pattern recognition.
2. Machine Learning A-Z™: Hands-On Python & R In Data Science
Python is one of the high-level programming languages. Those who are highly interested in machine learning this course is suggested to them. This is an overview of machine learning both in python and R.
Anyone who is not satisfied with their current job and want to become a data scientist to start a career in data science, this course is highly recommended to them. This tutorial will explore all the different fields of machine learning.
The purpose of this course is to teach the learner how to create machine learning algorithms in Python and R from data science experts. This is the BESTSELLER course.
if you want to be the master in machine learning on Python and R this course is for you. The instructors of this course are Kirill Eremenko and Hadelin de Ponteves. Both are great instructors. Super Data science team and super data science support also instruct this tutorial.
This instructor will be doing their job creatively to cover all the gaps of the learner. They also provide help for the better of the learning process. About 378,648 students have enrolled in this course and the rating is 4.5 out of 5.
From this online course, you will also learn how to handle advanced techniques like dimensionality reduction. You will come to know which machine learning model to choose which type of problem.
This course includes 41 hours on-demand video, 27 articles, 3 downloadable resources, full lifetime access Access on mobile and TV and certificate of completion. This online machine learning course not only provides you study materials but also develops your skills at every point.
People who want to start their career in this sector, choosing this machine learning course will be a great benefit for their career. They will learn how to build up accurate predictions. To make powerful analysis this course creates an added advantage.
3. Python for Data Science and Machine Learning Bootcamp
If you want to be a data scientist and want to learn to program, this is the course offered to you. This online course teaches the learner how to use Python for data science and machine learning by using the spark for big data analysis.
This course is designed for both beginners with some programming experience or experienced developers. That’s why this course became so easier for them to learn.
This machine learning tutorial is arranged with 22.5 hours on-demand video, 10 articles, 3 downloadable resources, full lifetime access Access on mobile and TV and certificate of completion.
About 18,3513 students have chosen this course and rated it 4.5 out of 5. This course is created by Jose Portilla, he is a data scientist and the founder of Pierian data. This course covers the understanding of machine learning in depth.
From this tutorial, you will learn how to create beautiful visualizations and use powerful machine learning algorithms. This is a course that is full of the segments that you need know to become a data scientist. Here you will learn the major topics of python.
This course also teaches you the use of NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit- Learn, Machine Learning and Tensorflow.
By doing this online course, you will come to know the random forest and decision trees. Besides, natural language processing and spam filters will also be taught to you in this tutorial.
Overall, this online machine learning tutorial also teaches you the fundamentals modeling method and improving Python programming skills.
At the end of the course, you will have a great overview of the World of data science and some technologies in different real-life scenarios. Learning all these things plays a vital role in future scope and you will be able to explore your own capability.
4. Machine Learning, Data Science and Deep Learning with Python
Machine learning is often incorporated into data science. Data science is a field of providing meanings information based on a large amount of complex Data. If you have high-level math skills you will easily become familiar with the fundamental concepts of machine learning data science and deep learning with Python.
This course is offered for the software developers and programmers who want to transition into the lucrative data science and machine learning career path. They will learn a lot from this training.
In this course, you will learn how to get an artificial neural network with tensorflow and Keras. Solve flow is an open source software library for data flow programming across a range of tasks.
This course is very important in machine learning. It creates a good opportunity for programmers who are willing to learn new skills in Python. To do this course, previous experience in coding is needed.
This tutorial covers 12 hours on-demand videos, 3 articles, full lifetime access, access on mobile and TV certificate of completion. About 82, 995 students have taken this course and rated it 4.5 out of 5.
Frank Kane is the founder of Sundog Education. This course instructed by him. Sundog Education by Frank Kane is joining the world in big data and machine learning.
From this course, you will learn how to make predictions using linear regression polynomial regression and multivariate regression. You will also learn how to classify image data and sentiments using deep learning.
In this course, you will understand reinforcement learning and how to build a Pac- Man-bot. You will able to build a movie recommender system using an item based and user-based collaborative filtering.
5. Bayesian Machine Learning in Python: A/B Testing.
This course recommended to the students and the professionals with a technical background who wants to learn Bayesian machine learning techniques to apply in their data science. For this course, you need to have an understanding in Python coding with the NumPy Stack and probability.
This Machine learning course is all about the concept of data science machine learning and Data analytics techniques for marketing digital media online advertising and more other sectors. Mathematical knowledge is recommended for this tutorial.
This course offers 5. 5 hours on-demand video, full lifetime access, access on mobile and TV and certificate of completion. The learner will learn the high level of ideas through Bayesian machine learning. Those people who are interested in this learning segment are suggested doing this course.
Nowadays A/B testing used in everywhere and this course is based on it. A/ B testing is the scientific method for evaluating and serving experiences, giving the decision that produces optimal results and quickly diminishing returns.
About 12667 students have already enrolled in this course and rated it 4.5 out of 5. lazy programmer Inc is the instructor of this course. He is the data scientist and big data engineer. The instructor will provide you some notes and help you to understand the math behind everything. Also, explain the concept and demonstrated using visual and realistic examples.
This online tutorial covers a wide range of topics from frequentist and Bayesian statistics. This course will teach you how to use adaptive Algorithms to improve A/B testing performance and you will understand the difference between them.
Finally, you will be able to apply Bayesian methods to A/B testing and understand the advanced method.
6. Cluster Analysis and Unsupervised Machine Learning in Python
This machine learning course is suggested to the people who want an introduction to unsupervised machine learning and cluster analysis.
If you have the knowledge about how to code in Python and NumPy, also know the way of installation, you can join in this course.
The purpose of this machine learning course is to teach the learner about data science techniques for pattern recognition, data timing, k- means clustering, hierarchical clustering, and KDE.
K- means clustering is a method of vector quantization, originally from data processing. Students will learn the regular k means algorithm from this course.
This course arranges with 5 hours on-demand video, full lifetime access, access on mobile and TV and certificate of completion. About 11611 students have enrolled in this course and rated it 4.6 out of 5.
Cluster analysis and unsupervised machine learning is an important term of data science. Lazy programmer lnc, the data scientist and big data engineer, is the instructor of this course.
The instructor explains, algorithmically, how hierarchical agglomerative clustering works. Also, make you understand how to read dendrogram.
In this course, you will understand the difference between single linkage, complete linkage, ward linkage, and WPGMA. You will also write a GMM in Python code.
This online course provides a good review and clear guidance on using castor oil for the real world and toy datasets. Also, it has a high-level overview of the cluster analysis method and Ana associate implementations.
7. The Complete Machine Learning Course with Python.
The motive of this course is to build a portfolio of 12 machine learning projects with Python, SVM, regression, unsupervised machine learning and more.
About 10391 students have taken this course and rated it 4.3 out of 5. This course offered with 18.5 hours on-demand video, six articles, 74 downloadable resources, full lifetime access, access on mobile and TV, and a certificate of completion.
For doing this course you need to have a basic Python programming knowledge and a good understanding of linear algebra.
If you are passionate about Python and willing to learn complete machine learning, then this course is for you. People, who were facing an interruption in the practical application of machine learning to solve real-world problems, are suggested doing this course.
The course teaches the students how to solve any problem in their business Jobs or personal life with power flow machine learning models.
They will also learn how to train machine learning algorithms to predict, identify handwritings, detect cancer cells and more.
Codestarts by Rob Percival will be teaching the next generation of coders. Anthony Ng, the algorithmic ding workshop recharger and conductor, is also an instructor of the course.
8. Build Machine Learning Applications: iOS Machine Learning Tutorial
If you are interested to build a more intelligent app using machine learning, this course is recommended to fulfill your wish.
For doing this tutorial, you need to have basic experience with iOS development OR, mobile development and a computer running OSX or macOS.
This course gets covered up with 6 hours 52 minutes on-demand video, extra material includes access on mobile and lifetime access.
The instructor of this course is Mark Price. He is the CEO of Developers. About 3974 students have taken this course and rated it 4.3 out of 5.
This course is designed basically for beginners. After completing the course, the certificate will be provided. From this tutorial, you will learn to code PROs code. Also, you will learn how to build real projects and apps that can make predictions. Besides, you will also study how to build amazing apps that can classify human handwriting.
9. Machine Learning Specialization
This is the series of specialization courses that helps a learner to master a skill. Specialization includes a hands-on project. By completing this course you will earn a certificate. That will play a vital role in your professional work Field.
Machine learning specialization is a combination of courses. This training is offered by the University of Washington. The purpose of it is to build intelligent applications. The specialization teaches the learner to the master machine learning fundamentals in four hands-on-courses.
You will come to know the major areas of machine learning about prediction, classification, clustering and information retrieval. Moreover, you will learn to analyze large and complex datasets that will help you to create intelligent applications to make predictions from data.
This is a 100% online course with a flexible schedule. This course is designed for the intermediate level learner and it takes approximately 8 months to complete.
Financial aid is also offered with this specialization for any student who cannot afford the tuition fees but interested in learning it. The instructors of this course are Carlos Guestrin and Emily Fox. Both are the Amazon professor of machine learning.
From this course, you will gain the skills of data clustering algorithms, machine learning, classification algorithms, and decision tree.
a. Machine Learning Foundations: A Case Study Approach
Machine learning foundations are the first part of the machine learning specialization. This course will be very helpful for a beginner and it provides a good foundation for the whole specialization and the advanced courses. As a beginner, you will learn the basics of machine learning. So this course highly recommended to the beginners before getting into the rest of the specialization.
After successfully completing this course, 33% of the students started a new career and 29% of students got a comprehensive career benefit. The rating of this course is 4.6 out of 5. This course takes approximately 24 hours to complete.
By doing this online course, you will gain the skills of Python programming, machine learning concepts, machine learning, and deep learning. This course is suggested to the learner who wants to learn how to implement machine learning models, how to train data to make out of machine learning.
In this course, the section of the case study approaches the students to teach the different concepts of machine learning. This section also provides the idea of the practical implementation of these concepts in real life.
b. Machine Learning: Regression
Regression is a statistical approach to find the relationship between variables. Regression is used in machine learning. It’s used to the outcome of an event. Doing this course you will be able to handle very sets of features and select between models of various complexity.
After completing this course, 41% of the students started a new career and 43% of the students got actual career benefit. About 22% of the students got a pay increase or promotion. The rating is 4.8 out of 5 and it takes approximately 36 hours to complete.
In this course, the students will acquire the skills of linear regression, ridge regression, lasso regression, and regression analysis. It covers all regression concepts. The training will help you to know the detail presentation of concepts and techniques of the traditional regression approach that are most important in today’s machine learning world.
c. Machine Learning: Classification
This is an impressive course of classification. This course is also a part of machine learning specialization. From this course, you will learn how to create classifiers that provides state of the art performance on various tasks. This course will teach you overall ideas and in-depth knowledge of classification.
Logistic regression, statistical regression, classification algorithms, decision tree are the skills you will learn from this tutorial. You can also understand that classification also have a real-valued or discrete input variable. A problem with two classes is often called in the two-class classification problem.
After finishing this course, about 48% of students started a new career and 46% of students got a tangible career benefit from this course. Also, 13% of the students got a pay increase or promotion. The course has a rating of 4.7 out of 5 and it takes approximately 42hours to complete.
d. Machine Learning: Clustering & Retrieval
Clustering and Retrieval is the last course that is included in the machine learning specialization. It is a great course to cover the techniques of retrieval data
About 35% of students have started a new career after completing this training and 36% have intangible career benefit from this course. The course rating is 4.6 out of 5 and it takes approximate 47 hours to complete.
From this online course, you will learn the skills of data clustering algorithms, k means clustering, machine learning, and KD tree.
10. Machine Learning Engineer Masters Program
This online course is the Master’s program for the machine learning engineers. Whoever wants to build their career as a machine learning engineer this course is specially offered for them.
The tutorial includes training on the latest advancement and technical approaches in artificial intelligence and machine learning. You will learn about deep learning, graphical model, and reinforcement learning by doing this online Masters program.
This course provides instructor-led live online classes. The classes have a flexible schedule to choose from.
According to Markets and Markets, USD 8.81 Billion Growth in machine learning market size by 2022. Annual growth is 119% that has increased in job postings for AI and ml experts.
There are 9 module includes in this course.
Introduction to python: You will learn various Python concepts in this section, like variables data types, operators, conditional statement and loop. The module provides an overview of Python.
Sequences and file operations: You will learn different types of sequence structures related operations and their usage. This module also teaches you diverse ways of opening, reading, and writing to files.
Deep dive- functions and OOPs: This section teaches you how to create a user-defined function and different object-oriented concepts. Like inheritance, polymorphism, overloading, etc.
Working with models and handling exceptions: You will come to know how to create generic Python scripts and exceptions in code.
Introduction to numpy and pandas: This module will help you to get familiar with the best statics, different types of measures and probability distributions
Data visualization: In this section, you will learn how to create simple plots like scatter plot histogram bar, etc.
Data manipulation: You will learn in detail about data manipulation.
GUI Programming: This section is a combination of life instructor-led training and self-paced learning.
Developing web Maps and representing information using plots: In this section, you will understand how to design Python applications.
Computer vision using open CV and visualization using bokeh: You will also learn designing Python application in the section.
11. Machine Learning Certification Training using Python
This certification training course of python will teach you the core concepts about python. It is designed to make you grab the idea of machine learning.
According to Forbes, roles like Chief Data Scientist & Chief Analytics Officers have emerged to ensure that analytical insights drive business strategies.
Kdnuggets predicts that businesses Will Need One Million Data Scientists by 2020.
By doing this course you will develop skills in data processing, dimensional reduction and also various different sectors. The course rating is around 4.5 out o 5.
This certification course also teaches you to learn tools and techniques for productive modeling, validate machine learning algorithms and time series related concepts.
In professionals sector, the effect of this course is also benefited. Developers, analytic managers, business analysts, information architects, Python professionals, any other related professional can easily fit into this online training.
12. Masterclass on What is Machine Learning: iOS Developers Cheatsheet
To become a successful web developer and create new ideas to develop apps you have to learn some extraordinary courses. This course is one of them.
If you have some knowledge of ios and a basic understanding of coding, also if you want to build a career in this sector, this course is highly suggested for you.
This course includes 1 hour 32m on-demand video, access on mobile and lifetime access. By completing this training you will know about ios development and understand machine learning.
From this course, you will also learn how to start working with ios machine learning and how to train Al.
About 5743 students have already enrolled in this course and the rating is around 4.3 out of 5. Yohann Taieb is the instructor of this course and he is a apps developer. This machine learning course is highly popular.
This machine learning course is designed for beginners level students. After completing this course successfully the students will get a certificate of completion.
13. Applied Machine Learning in Python
This online tutorial course is the part of the Applied Data Science with Python Specialization. This course is offered by the University of Michigan.
This online tutorial also offers 10 days free trial. So if you are not interested to continue then within these days you can drop this course. Financial aid is also available for students who can not afford the tuition fees.
The instructor of this course is Kevyn Collins-Thompson. He is the Associate professor in the School of Information. This tutorial course is offered for the intermediate level learner and the rating is 4.6 out of 5. This course takes approximate 24 hours to complete within flexible deadlines.
From this course, you will gain the skills of Python Programming, Machine Learning (ML) Algorithms, Machine Learning, Scikit-Learn. You will learn how to build features that build analysis need. Also, You will learn how to Create and evaluate data clusters.
This course will teach you how to explain different approaches for creating predictive models and describe how machine learning is different than descriptive statistics.
14. Google Cloud Platform Big Data and Machine Learning Fundamentals
If you are looking for the course that will teach you more-in-depth about the technical features and help you to design and build data processing systems on the Google cloud platform, then this course is the right course for you.
Google Cloud Platform Fundamentals course will introduce you to data and machine learning fundamentals.
This tutorial course is a part of the data engineering on Google cloud platform specialization. And it is offered by Google cloud. The course rating is 4.6 out of 5.
By doing this fundamental machine learning course 51% of the students have started a new career and 47% of the students have got a tangible carrier to benefit from this course.
In this machine learning course, you will gain the skills of TensorFlow, big query, Google cloud platform, and cloud computing. It helps the students to understand about the cloud platforms and how they work for analytics.
This course is a complete online course with flexible deadlines. It is designed basically for the intermediate level learner and it takes approximately 10 hours to complete. This course is very informative and help students to enrich their knowledge.
This online course also offers financial aid to the students. If any student wants to do this course but cannot afford the tuition fees then it provides financial aid to him.
If anyone wants to become a Google certified professional data engineer then this course will be the best for them to jump into the next level of their career goal.
15. Introduction to Machine Learning for Data Science
If you are interested in the IT industry and want to work as a developer then this is the right course for you. This course will help you to learn a lot. It will introduce you to a quick overview of machine learning and the process.
People, who are interested in understanding how machine learning is used for data science, are highly recommended to do this course. It is about a high view of machine learning and data science.
To do this course you need to have basic knowledge of high school level mathematics and need to be highly motivated to learn. Basic computer skill is also required to do this course.
This tutorial offers 5.5 hours of on-demand video, one article, and six downloadable resources. It also offers students lifetime access and access on mobile and TV. At the end of the training, the student will earn a certificate if they complete it successfully.
About 25,131 students have enrolled in this course and the rating is 4.2 out of 5. David Valentine is the Backyard data scientist. He is the instructor of this course. The instructor explains the concepts very well and presents the content very clearly for better understanding.
This course is a good understanding of what machine learning is about and what people can achieve by using it. In this tutorial, you will learn about computer science, algorithms, big data, artificial intelligence, and data science. It will help you to understand how these different domains work together.
You will understand the impact of machine learning and data science is having in our society and how that technology has changed the world.
16. Introduction to Machine Learning & Deep Learning in Python
This machine learning course is designed for the beginner level students who have no previous experience in machine learning but looking for a quick refresher. The introduction to machine learning and deep learning in Python is highly recommended to them.
In this course, you will learn how to solve regression problems and classification problems. You will learn the use of neural networks and more up to date machine learning techniques. For doing this course you need to have previous experience in Python.
This deep Learning course covers 13 hours on-demand video, 14 articles, full lifetime access, Access on mobile and TV and certificate of completion. This course provides various machine learning techniques that are needed for the applications in the industry.
About 4,449 students have taken this course and rated it 4.2 out of 5. The instructor of this course is Holczer Balazs and he is a software engineer.
17. Data Science: Supervised Machine Learning in Python
People, who are interested to learn classic data science and machine learning algorithms, are suggested to do this course. It is a great tutorial for learning that the end to end concepts of Machine Learning. The professional also offered to do this course if they want to apply machine learning techniques to real-world problems.
It includes 6 hours on-demand video, full lifetime access, access on mobile and TV, and certificate of completion.
The purpose of this course is to provide the learner a complete guide on implementing machine learning algorithm in Python and with Sci-Kit Learn. It course explains all materials in an engaging manner.
About 10,332 students have chosen this course and rated it 4.6 out of 5. Lazy Programmer lnc. is the data science and big data engineer. This course instructed by him. To do this course you need to be familiar with Python, numpy and pandas.
Also, you need to have some ideas about probability and statistics, and the strong ability to write algorithms. The course provides get content with examples.
This course helps the learner to understand and implement k- nearest neighbors in Python, the limitation of KNN and Bayes Classification.
After learning algorithms, you will learn about the practical machine learning topics like Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.
18. Ensemble Machine Learning in Python: Random Forest, AdaBoost
This course is the most insightful machine learning tutorial. If you want to learn about ensemble to deep machine learning this course is proposed for you.
Boosting, Bagging, and statistical machine learning for data science in python, various other methods of ensemble you will learn from here.
7120 students have chosen this course and rated it 4.6 out of 5. Lazy Programmer Inc. is the instructor of this course. Lazy Programmer is the data scientist, big data engineer, and also a full-stack software engineer.
This course is covered by 5.5 on-demand videos, full lifetime access, access on mobile and TV and Certification of Completion.
By doing this course you will understand and derive the bias-variance decomposition. This course will also help you to learn about using deep reinforcement learning.
To understand and implement AdaBoost, and random forest this online tutorial is preferable.
19. Machine Learning with TensorFlow on Google Cloud Platform Specialization
If you think you are experienced in machine learning and want to specialize yourself in Tensorflow on Google cloud platform, this course recommended for you.
This course is all about learning ML with Google cloud. Real -world experimentation with an end to end ML.
Tensorflow, machine learning, Feature engineering, Cloud computing are all the skills you will learn from this online specialization course.
a. How Google does Machine Learning
This is one of the courses of Machine Learning with TensorFlow on Google Cloud Platform Specialization. This intermediate level course takes around 10 hours to complete. It has a rating of 4.5 out of 5.
From this course, you gain skills on Application Programming Interfaces (API), Inclusive ML, Machine Learning, Google Cloud Platform, Bigquery. You will learn the answers to what, why and how in machine learning in relation to Google.
With this training. you will be taking the initiative of this specialization. The google expert will teach you everything you need to know about Google and Machine Learning.
b. Launching into Machine Learning
This is the second course under the Machine Learning with TensorFlow on Google Cloud Platform Specialization. It has a rating of 4.5 out of 5. This intermediate level course takes about 7 hours to complete.
In this specialization course, you will acquire knowledge about TensorFlow, Bigquery, machine learning, data cleansing. You will understand the models on loss functions and performance metrics. common problems of Mchine Learnings, all the related things of datasets.
The core components of TensorFlow and you will get hands-on practice building machine learning programs.
c. Intro to TensorFlow
This course is offered to create machine learning models in TensorFlow and to use the TensorFlow libraries to solve numerical problems.
Application Programming Interfaces (API), EstimatorMachine Learning, Tensorflow, Cloud Computing are all the skills that you will learn from this tutorial.
Scaling TensorFlow models with CMLE will develop your training through this course. The rating of this online course is 4.4 out of 5. It takes around 9 hours to complete.
d. Feature Engineering
This course is also part of the specialization. This is the only course that teaches featuring engineering and focuses on production issues and dataflow.
This course is offered by Google cloud and rating is 4.4 out of 5. This intermediate course takes around 10 hours to complete.
You will get hands-on practical choosing courses. In the future, you can use the references for the code solutions that you will do with the help of the instructors.
e. Art and Science of Machine Learning
From the art and science of machine learning course, you will learn the essential skills of ML intuition, good judgment, and experimentation.
Tensorflow, Machine Learning, Cloud Computing, Estimator skills are the main focus here. You not only study these skills from this course but also learn many knobs and levers involved in training a model.
This specialization course has a rating of 4.4 out of 5. It takes approximately 10 hours to complete.
20. Machine Learning with Mahout Certification Training
In this modern technical age of exponential data, machine learning growth is increasing day by day. From this Machine Learning and Mahout Certification Training course, you will come to know the importance of this.
If you are a Data Scientists and looking to smooth your machine learning skills, this course is suggested to you.
This course covers the fundamentals of machine learning techniques in various algorithms of support vector machines, random forests, mahout on Hadoop and Amazon EMR.
This certification training course will help you gain experience with real-life case studies and assignments. You will get lifetime access, peer interaction forum, along with a completion certificate after finishing this tutorial.
21. Structuring Machine Learning Projects
If you are interested in error analyzing and different kinds of learning techniques, this course is suggested to you. you will learn machine learning projects perfectly by doing this online course.
Machine Learning, Deep Learning, Inductive Transfer, Multi-Task Learning – all these terms of skills you will learn from this course.
Andrew Ng, Kian Katanforoosh and Younes Bensouda Mourri are the instructors of this course. Their guideline will help you a lot for better learning. This tutorial has a rating of 4.8 out of 5. this 100% online course takes around 7 hours to complete.
22. Serverless Machine Learning with Tensorflow on Google Cloud Platform
This course is a part of Data Engineering on Google Cloud Platform Specialization. If you not only want to build an ML model using Tensorflow but also interested to know the importance of processing and combining features, then this online training course is proposed to you.
From this online course, you will be able to develop your skills at machine learning, Google cloud platform, Feature Engineering, TensorFlow, and cloud computing.
To do this certification course you need to have basic proficiency with common query language such as SQL. Also, you need to have some experience with data modeling, exact transform, load activities.
This tutorial has a rating of 4.4 out of 5. This intermediate level course takes around 11 hours to complete.
23. Practical Machine Learning
This course is all about practical exercises on machine learning. Three biostatistics professor and Ph.D. holders Jeff Leek, Roger D.peng, Brian Caffo are the instructors of this specialization course.
Random forest, Machine learning algorithms, R programming are all the skills that you will acquire after completing this course. It is a hand on learning course.
This course has a rating of 4.5 out of 5. It takes around 14 hours to complete. After doing this tutorial you will understand machine learning methods like regression or classification trees, the complete process of building prediction functions, applying prediction functions, and many other related things.
24. Machine Learning Practical: 6 Real-World Applications
If you want to build your career in this challenging sector and be yourself as a machine learning practitioner, here is the best course that is highly recommended for you.
This course is all about practice on real applications and real-life practices. This training course is instructed by Kirill Eremenko, Hadelin de Ponteves, and Dr. Ryan Ahmed. They all are in SuperDataScience Team.
This course will cover 8.5 on-demand videos, 1 article, full lifetime access, access on mobile and TV and certification of completion. 3866 students studied in this course and rated it 4.3 out of 5.
If your goal is to become an expert in data science and machine learning, you can choose this course to build your career structure. This course will provide you all the deep learning techniques and teach you to build a project.
25. Data Science and Machine Learning Bootcamp with R
This certification course is about using R for machine learning algorithms, Data science, Data analysis and to create data visualization.
You will be able to know various topics and clear explanation from this course. You will also learn about advanced data features.
This course includes 17.5 hours on-demand video, 8 articles, 3 downloadable videos, Full lifetime access, Access on mobile and T and certificate of completion.
35505 students have enrolled in this course and rated it 4.5 out of 5. Data scientist Jose Portilla is the instructor of this course. The good learning curve and lots of features will be discussed throughout this course.
This course will also teach you how to do data mining in twitter and linear regression.
26. From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase
This online practical course is for those who want to learn machine learning in a deep core and want to apply it for real life.
Tech executive, investors, whoever is interested in big data machine learning, can do this course. 8271 students have already enrolled in this course and rated it 3.9 out of 5.
From this course, you will learn how to identify situations and solve them with an appropriate solution. Loony Corn is the instructor of this course.
This course arranged 20 hours on-demand video, 1 article, 42 downloadable resources, Full lifetime access, Access on mobile and TV and certificate of completion.
27. Advanced Machine Learning Specialization
Advanced machine learning specialization course is an online certification course that will help you to master in the technical skills.
This course is a deep learning specialization. There are 7 courses in this specialization. A large number of experienced instructors will help you in completing this course.
You will learn about the practical and theoretical knowledge from this course and also hand on experience from these instructors.
a. Introduction to Deep Learning
This is the first course under the Advanced machine learning specialization. It is about deep learning. So you need to have basic knowledge of Python and linear algebra and probability.
This is an advanced level course that takes around 37 hours to complete. You need to understand Linear regression, Logistic regression, the problems of overfitting, regularization.
Evgeny Sokolov, Andrei Zimovnov, Alexander Panin, Ekaterina Lobacheva, and Nikita Kazeev are the group of instructors of this training.
This part of the specialization will teach you the skills of Recurrent Neural Network, Tensorflow, Convolutional Neural Network, and Deep Learning.
The main motive of this course is to give the learners the core understanding of modern neural networks and their practical application. You will thoroughly learn about all the popular blocks of the neural network.
b. How to Win a Data Science Competition: Learn from Top Kagglers
In this course, you will learn to analyze and solve competitively predictive modeling tasks. This course is very popular among the students and the rating is 4.7 out of 5. The whole 100% online course is about 50 hours long.
Dmitry Ulyanov, Alexander Guschin, Mikhail Trofimov, Dmitry Altukhov, and Marios Michailidis are the expert group of instructors of this online training.
You need to have previous knowledge of Python and Machin learning for this advanced level course. It highly focuses on practical machine learning methods rather than theoretical implications.
After finishing this course you will have experience in analyzing and data interpreting. You will gain knowledge of different algorithms and learn how to efficiently tune performances.
Data analysis, Feature Extraction, Feature Engineering, and Xgboost are the skills that you will acquire from this course.
c. Bayesian Methods for Machine Learning
This is the third course under the Advanced machine learning specialization. The Bayesian method is highly applicable to various sectors. This online tutorial will teach you the basics of that method. You will also learn to apply and observe how the method works.
This 100% online course is designed for advanced learners. You need to go through the previous courses under this specialization to understand this course better. It takes around 41 hours to complete this whole tutorial.
This online Machine Learning course has a rating of 4.6 out of 5. Researcher duo Daniil Polykovskiy and Alexander Novikov are the instructors of this tutorial.
Bayesian optimization, Gaussian Process, Markov Chain Monte Carlo, Variational Bayesian Methods all skills you will know from this course. This course assumes high proficiency with TensorFlow, Keras, and Python.
d. Practical Reinforcement Learning
This course is about the practical application of Reinforcement Learning. Here, you will learn about the problems of reinforcement learning and find out the proper solution to it.
This advanced level online course takes approximately 39 hours to complete. It has a rating of 4.2 out of 5. Pavel Shvechikov and Alexender Panin are the instructors of this course.
This tutorial will teach you the fundamentals of RL method. You will also learn to use Deep Neural Networks and RL algorithms. You will also be able to use RL to play games.
Models free method will teach you about ideas of real-world problems. Approximate value based methods are also included here.
e. Deep Learning in Computer Vision
The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. From this course, students will learn how to build a face recognition system.
This advanced level training program takes around 28 hours to complete. It has a rating of 4.0 out of 5. Senior lecturers of Computer Science Anton Konushin and Alexey Artmov are the instructor here.
You will be introduced to image processing and computer vision. These simple image processing methods solve as buildings blocks for all the deep learning employed field of this computer vision.
Convolutional features for visual recognition will teach you the detection and classification of facial attributes also face verification.
This course also includes videos on object detection, object tracking, and action recognition. You will also learn about image segmentation and synthesis. This will help you to generative adversarial networks and image transformation with neural networks.
f. Natural Language Processing
This course is the last part of the Advanced machine learning specialization. It is designed to teach natural language processing. There have a great review about this course and the rating is 4.7 out of 5.
You will learn chatterbox, TensorFlow, deep learning, natural language processing from this course. This course is based on practical assignments and projects.
This online course starts with intro and text classification. You will get the idea of linear models for sentiment analysis and networks about words and characters.
This tutorial will also teach about Language modeling and sequence tagging. You will learn how to predict a sequence of tags for sequences of words, vector space models of semantics, sequence to sequence tasks, dialog systems, and many other things.
Anna Potapenko, Alexey Zobnin, Anna Kozlova, Sergey Yudin, and Andrei Zimovnov are the group of instructors who will conduct this course as per their expertise.
g. Addressing Large Hadron Collider Challenges by Machine Learning
This course will give you the opportunity to apply your skills in the search for the new physics using advanced data analysis techniques. This course is suggested for newly graduated students.
The lesson starts with the introduction of particle physics for a data scientist. That will help you testing hypotheses experimentally. Particle identification is also a skill that you learn from this course. It describes several detectors in high energy physics.
Physics in rare decays will explain to you how new physics search can be mediated through a search for are processes. Search for dark matter hints with machine learning at a new CERN experiment. Detector optimization is also an important term that you will learn about while doing this course.
This online tutorial has a rating of 4.7 out of 5. It takes around 23 hours to complete. Expert instructors Andrei Ustyuzhanin and Mikhail Hushchyn will be conducting this training.
28. Data Science & Machine Learning using Python – A Bootcamp
If you want to build your career in the IT field and want to improve your ability then the course will be very helpful. Especially for the newcomer, this course is highly suggested.
Dr. Junaid Qazi is a data scientist and this course is instructed by him. 680 students have enrolled in this course and rated it 4.4 out of 5. By doing this course you will be able to get into the demanding career of data science and machine learning.
This online certification course includes 24.5 hours on-demand video, 3 articles, 1 downloadable resource, Full lifetime access, Access on mobile and TV and Certificate of completion.
A huge term of factors will be taught through this learning process. Statistical plots with seaborn, pandas for data analysis, numpy, numerical data, basic plotting with matplotlib, and object-oriented approach that will improve your skills.
29. Understanding Machine Learning with R
The objectives of this course are to introduce you to the uses of machine learning with R. Basically this is a type of test where machine learning solution. This is the best machine learning course.
Jerry Kurata is a Solutions Architect at In Step Technologies and he is the instructor of this course. He did not only guide you but also help you to proper learning.
From this course, you’ll learn how to apply machine learning to solve problems that are difficult, and some might say impossible to solve with the standard. This beginner level tutorial has a rating of around 4.5 out of 5. It is 1 hour 25 minutes long.
You will learn how developers and data scientists use machine learning to predict events based on data. This course walks through the process of creating a machine learning prediction solution.
30. Introduction to Machine Learning with ENCOG 3
This is the course that is focused on the implementation and applications of various machine learning methods. So anyone who is interested in this site is recommended to do this course.
The best part of this course is you will be offered 10 days of free trial class. So if you do not find it interesting after starting it, you can drop this course. For that, you don’t need to pay.
The online course will try to make a base foundation first by explaining machine learning through some real-world applications and various associated components.
This module is on neural network components in ENCOG for. NET. From this module, you will learn basic propagation training models.
You will also learn about various kind of data processing that is required to process data in such a manner so that it can be used by neural network models efficiently.
31. Machine Learning and Microsoft Cognitive Services
This course is about Microsoft cognitive services. Microsoft’s Cognitive Service APIs offer easy-to-use machine learning models that are trained on vast repositories of data to offer solutions for common use cases.
This course will show how you can use APIs for text, image, and video moderation. So interested students who will want to increase their knowledge are offered to do this course.
You will learn how to harness the power of AI using Microsoft Cognitive Services and see real examples of integrating with these APIs. the name of the instructor of this tutorial is Janani Ravi.
She will teach you everything you need to know about Microsoft Cognitive service. It is a beginner level course with a rating of around 4.6 out of 5. This online course takes around 2 hours to complete.
You will be able to use APIs for text, image, video moderation. You will have hands-on experience using pre-built machine learning models
32. Neural Networks for Machine Learning From Scratch
This course is about understanding neural networks. If you have basic experience in Python and calculus this course will be easier for you.
Anyone who wants to learn the mathematical background of neural networks and deep learning and also interested in Data Science, Artificial Intelligence and Machine Learning this course highly suggested for them.
To Develop your own deep learning framework from zero to one with hands-on Machine Learning with Python, you are offered to do this training course.
320 students have enrolled in this course and rated 4.8 out of 5. Sefik llkin Serengil, who is a software developer, is the instructor of this course.
This course includes 3 hours on-demand video, 2 downloadable resources, Full lifetime access, Access on mobile and TV, Assignment and Certificate of Completion.
33. Machine Learning A-Z: Become Kaggle Master
This course is recommended for those who want to become a data scientist. 3754 students have already enrolled in this course and rated it 4.3 out of 5.
This online course covers up with 36.5 hours on-demand video, 25 downloadable resources, Full lifetime access, Access on mobile and TV. You will receive a Certificate of Completion after this training.
The tutorial has been designed by IIT professionals who have mastered in Mathematics and Data Science. Techlov pvt limited Bigdata and Analytics is the instructor of this course.
All Advanced Level Machine Learning Algorithms and Techniques like Regularisations, Boosting, Bagging and many more included in this course and completing this you will learn all of these.
You can also learn how to Statistical concepts make Ninza in Machine Learning and to use Numpy and Pandas for Data Analysis.
34. Mathematics for Machine Learning Specialization
This is a specialization course about Mathematics for Machine Learning. The objectives of this course are the prerequisite mathematics for applications in data science and machine learning.
Throughout this training, you will acquire the skills on Eigenvalues And Eigenvectors, Principal Component Analysis (PCA), Multivariable Calculus, and Linear Algebra skills.
Every Specialization includes a hands-on project and the instructor will train you to do this.
At the end of this specialization, you will have the essential mathematical knowledge to take more advanced courses in machine learning and boost your carer up.
a. Mathematics for Machine Learning: Linear Algebra
This course is the first part of the specialization course. It will teach you how linear algebra is relevant to machine learning and data science.
The skills of Eigenvalues And Eigenvectors, Basis (Linear Algebra), Transformation Matrix, Linear Algebra will be taught to you in this course.
This 100% online course has a rating of 4.6 out of 5. This is a beginner level course with a rating of 4.6 out of 5. It takes around 21 hours to complete.
You will also learn Vectors are objects that move around space. That will help you with Basis, vector space, and linear independence and Applications of changing basis. The whole course keeps focussing only on the concept which makes it short.
b. Mathematics for Machine Learning: Multivariate Calculus
In this tutorial, you will learn about Linear Regression, Vector Calculus, Multivariable Calculus, Gradient Descent and have proper skills in these fields. After completing you will understand multivariate calculus in machine learning with differential examples and special cases.
This beginner level course has a rating of 4.7 0ut 0f 5. It takes around 22 hours to complete. Samuel J. Cooper, David Dye, and A. Freddie Page are the expert instructors of this training program.
The basic foundation of with variables, constants, and context. Multivariate chain rule and its applications will make you understand the conceptual structure of machine learning. Taylor series, linearization, intro to optimization and regression are also included in this course.
c. Mathematics for Machine Learning: PCA
This is the last course offered under the Mathematics for Machine Learning Specialization. This is an intermediate level course. So you need to have clear ideas about the topics taught in the previous two courses. This course is about the mathematical foundations for a fundamental dimensionality reduction technique, PCA.
Python programming, principal component analysis, projection matrix, mathematical optimization are the skills that you will learn from this course.
This tutorial has a rating of 4.0 out of 5. This 100% online course takes around 18 hours to complete. Marc P. Deisenroth is the instructor of this course. He is a lecturer in Statistical Machin Learning.
To have a complete understanding of the lectures and exercises, you need to have some ability of abstract thinking, basic knowledge of multivariate calculus, python programming, and numpy. At the end of this course, you’ll be familiar with important mathematical concepts and implement PCA.
35. Understanding Machine Learning
This online tutorial offers 10 days of free trial. So if you are not interested to continue, then you can drop this course. This tutorial course has a rating of around 4.5 out of 5. This course takes 39 minutes to complete.
This machine learning online course completely designed for beginners. So no prior knowledge is required to do this course.
This machine learning course is designed by David Chappell. He is the Principal of Chappell & Associates in San Francisco, California. David has been the keynote speaker for more than a hundred events, and his seminars have been attended by tens of thousands of people in forty-five countries. He is also the instructor of this course.
From this course, you will learn the basics of machine learning. During this the machine learning process, you will understand all the topic that you need to know.
You will learn how to explore the open source programming language R, learn about training and testing a model as well as using a model. After completing this course properly you will have a clear understanding of exactly what machine learning is all about.
36. Understanding Machine Learning with Python
Understanding Machine Learning with Python is the course that teaches the learner how to use data to predict future events with the help of machine learning.
Through this course, the learner will learn about the process of creating a machine learning prediction solution and will be introduced Python, the sci-kit-learn library, and the Jupyter Notebook environment.
This online tutorial offers 10 days of free trial. So if you are not interested to continue within these days you can drop this course. The rating of this tutorial is around 4.5 out of 5. This course takes 1 hour 54 minutes to complete.
This machine learning online course completely designed for beginners. So there is no prior knowledge required to do this course.
Jerry Kurata is the instructor of this Python machine learning course. He is a Solutions Architect at InStep Technologies
Finally, after completing the course, learners will be able to use Python and the sci-kit-learn library to create Machine Learning solutions.
37. Getting Started with Azure Machine Learning
To Predict weather, route and the current status of traffic jams machine learning is the way to build about this concept. People who are interested in learning about the method are suggested to do this course.
This online tutorial offers 10 days of free trial. So if you are not interested to continue then within these days you can drop this course. This tutorial has a rating of around 4.5 out of 5. This course takes 2 hours 14 minutes to complete.
This machine learning online course completely designed for beginners. So there is no prior knowledge required to do this training.
Jerry Kurata is the instructor of this machine learning course. He is a Solutions Architect at InStep Technologies
By the end of this course, you’ll know how to create, deploy, and maintain machine-learning solutions in Azure and make their predictive capabilities available to the users.
38. How to Think About Machine Learning Algorithms
This online tutorial offers 10 days of free trial. So if you are not interested to continue then after ten days you can drop this course. The rating of this tutorial course is around 4.5 out of 5. It takes 3 hours 8 minutes to complete.
This machine learning algorithms online course is completely designed for beginners. So there is no prior knowledge is required to do this course. Swetha Kolalapudi is the instructor here. She is an alumnus of top schools like IIT Madras and IIM Ahmedabad.
You’ll learn how to identify those situations to provide an appropriate solution with machine learning. First, you will learn how to solve the problem: classification, regression, clustering or recommendation. then you will learn to set up the problems statements, features, and labels. Lastly, you are able to plug in a standard algorithm.
At the end of this course, you’ll have the skills and knowledge required to recognize an opportunity for a machine learning application and seize it.
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