Top 10 Machine Learning and Deep Learning Certifications & Courses Online in 2023

Machine learning and deep learning are two related but distinct fields of artificial intelligence (AI).

Machine learning is a subfield of AI that involves teaching machines to learn from data, without being explicitly programmed to do so. Machine learning algorithms can be classified into three broad categories: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the machine is trained on a labeled dataset, meaning that each example in the dataset is already tagged with the correct output. The machine learns to predict the output for new, unseen examples based on the patterns it has learned from the training data. Unsupervised learning, on the other hand, involves training the machine on an unlabeled dataset, meaning that there are no predefined outputs. The machine learns to identify patterns and relationships in the data, which can be used for clustering or other purposes. Reinforcement learning involves training the machine to make decisions in a dynamic environment, based on feedback it receives from its actions.

Deep learning is a subfield of machine learning that involves training artificial neural networks, which are inspired by the structure and function of the human brain. Deep learning networks are composed of many layers of interconnected nodes (neurons), which are trained to recognize patterns in data. Deep learning has been particularly successful in computer vision, natural language processing, and speech recognition applications.

1. Machine Learning Specialization(Coursera)

The Machine Learning Specialization is a set of online courses offered by Coursera in collaboration with the University of Washington. The specialization consists of 4 courses and a capstone project, and it is designed to provide learners with a comprehensive introduction to machine learning and its applications.

The specialization covers the following topics:

1. Machine Learning Foundations: This course covers the basics of machine learning, including supervised and unsupervised learning, linear regression, classification, clustering, and evaluation of machine learning models.
2. Regression: This course covers advanced topics in linear regression, including regularization, feature selection, and model selection.
3. Classification: This course covers advanced topics in classification, including logistic regression, decision trees, and support vector machines.
4. Clustering & Retrieval: This course covers clustering techniques, including k-means, hierarchical clustering, and spectral clustering, and retrieval techniques, including nearest neighbor search, locality-sensitive hashing, and inverted indexing.
5. Capstone Project: This project allows learners to apply their knowledge of machine learning to a real-world problem, such as image recognition or natural language processing.

The courses include video lectures, quizzes, and programming assignments to help learners apply their knowledge to real-world problems. Learners also have access to a community of learners and mentors to interact and share ideas.

The specialization is taught by experienced instructors and practitioners in the field of machine learning. Upon completion of the specialization and the capstone project, learners receive a certificate of completion from Coursera. The specialization is highly recommended for individuals who want to develop a solid foundation in machine learning and its applications.

Key Highlights

• Learn Silicon Valley’s best practices in innovation in the field of Machine Learning and AI
• Learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications
• Gain Logistic Regression, Artificial Neural Networks skills. Implement your own neural network for digit recognition.
• Numerous case studies and applications for practical training and insights into solving real world problems
• Learn how to apply machine learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas
• Flexible deadlines and opportunity to learn at your own pace and schedule
• Dozens of code notebooks with code samples and interactive graphs to help you complete graded assignments

Duration : Approx 3 months, 9 hours per week
Rating : 4.9
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2. Deep Learning Certification by deeplearning.ai (Coursera)

The Deep Learning Certification by deeplearning.ai is a series of online courses offered by deeplearning.ai, which is an education company founded by Andrew Ng, a well-known figure in the machine learning and deep learning communities.

The certification program consists of five courses that cover the fundamentals of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, natural language processing, and more. The courses are self-paced and can be taken online, which makes them accessible to anyone with an internet connection.

The program culminates in a final project, where students apply their knowledge to solve a real-world problem using deep learning techniques. Upon completion of the program, students receive a certificate that can be added to their resume

The program culminates in a final project, where students apply their knowledge to solve a real-world problem using deep learning techniques. Upon completion of the program, students receive a certificate that can be added to their resume or LinkedIn profile to showcase their skills in deep learning.

Overall, the Deep Learning Certification by deeplearning.ai is a great way for aspiring data scientists and machine learning engineers to gain practical skills in deep learning and advance their careers in the field.

Key Highlights

• Learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization
• Work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing
• Interviews of many top leaders in Deep Learning
• Programming assignments to help you practice the ideas and techniques learnt
• Gain insights and career advice from best in the industry
• Rated as best Coursera deep learning certification

Duration : Approx 3 months, 11 hours per week
Rating : 4.9
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3. Machine Learning Nanodegree Program (Udacity)

The Machine Learning Nanodegree Program offered by Udacity is an online program that provides students with a comprehensive understanding of machine learning, from the fundamentals to advanced techniques. The program is designed to prepare students for careers in data science and machine learning engineering.

The program covers topics such as supervised and unsupervised learning, deep learning, natural language processing, and more. Students will work on several projects throughout the program, including building a customer segmentation model for a real e-commerce company, building an image classifier, and developing a sentiment analysis model.

The program is self-paced, meaning students can complete it on their own schedule, and it is designed for learners of all backgrounds, including those with no prior experience in programming or machine learning. Students receive mentorship and support from industry professionals throughout the program, and upon completion, they receive a certificate that can be added to their resume or LinkedIn profile to showcase their skills.

Key Highlights

• Immersive content and real world projects from industry experts
• Learn popular frameworks like Sklearn, Tensorflow, and Keras
• In-lecture quizzes for practice
• Learn practical industry best practices to be well equipped in the job market
• 1-on-1 technical mentor who will answer your questions and guide your learning
• Access to career coaching services, interview prep advice from professionals and resume review
• Flexibility to learn at your own pace and schedule
• Student support community to exchange ideas and clarify doubts

Duration : 3 months, 10 hours per week
Rating : 4.8
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4. Machine Learning A-Z™: Hands-On Python & R in Data Science (Udemy)

The Machine Learning A-Z™: Hands-On Python & R in Data Science is an online course offered by Udemy that aims to teach students the fundamentals of machine learning using both Python and R programming languages. The course is designed for beginners with little to no prior knowledge of machine learning and provides a hands-on approach to learning.

The course covers a wide range of topics, including data preprocessing, regression, classification, clustering, association rule learning, reinforcement learning, and more. Students will work on several practical projects throughout the course, such as building a movie recommendation system and a customer churn prediction model.

The course is self-paced, meaning students can learn at their own speed, and it is accessible to learners of all backgrounds. The course includes over 40 hours of video content, as well as quizzes and assignments to test and reinforce students’ understanding of the material.

Overall, the Machine Learning A-Z™: Hands-On Python & R in Data Science course is an excellent choice for beginners looking to gain practical skills in machine learning using Python and R programming languages. The course provides a comprehensive overview of machine learning techniques and offers hands-on experience building real-world projects, making it a valuable investment for aspiring data scientists and machine learning engineers.

Key Highlights

• Build powerful Machine Learning models and know how to combine them to solve any problem
• Know which Machine Learning model to choose for each type of problem
• Hands-on Practical and interactive exercises based on real life examples to learn building your own models
• Python and R code templates that you can download and use on your own projects
• Handle complex topics like Reinforcement Learning, NLP and Deep Learning
• Comprehensive Q&A Section that addresses most of the commonly encountered issues

Duration : 41 hours on-demand video, 31 articles, 5 downloadable resources
Rating : 4.5
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5. Professional Certificate in Deep Learning by IBM (edX)

The Professional Certificate in Deep Learning by IBM is an online program offered through edX that provides students with a comprehensive understanding of deep learning, from the fundamentals to advanced techniques. The program is designed for learners of all backgrounds, including those with no prior experience in programming or deep learning.

The program consists of five courses that cover topics such as neural networks, convolutional neural networks, recurrent neural networks, natural language processing, and more. Each course includes hands-on projects where students will work on building deep learning models using popular frameworks such as TensorFlow and Keras.

The program is self-paced, meaning students can complete it on their own schedule, and it is designed to take around six months to complete. Students receive mentorship and support from industry professionals throughout the program, and upon completion, they receive a certificate that can be added to their resume or LinkedIn profile to showcase their skills in deep learning.

Key Highlights

• Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders
• Master Deep Learning at scale by leveraging GPU accelerated hardware for image and video processing, as well as object recognition in Computer Vision
• Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, text analytics, Natural Language Processing, recommender systems, and other types of classifiers
• Series of hands-on labs, assignments, and projects inspired by real world challenges and data sets from the industry

Duration : 5 courses, 5 to 6 weeks per course, 2 – 4 hours per week
Rating : 4.6
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6. Machine Learning Specialization by University of Washington (Coursera)

The Machine Learning Specialization offered by the University of Washington is an online program available on Coursera that covers a comprehensive range of machine learning concepts, algorithms, and techniques. The program consists of four courses, each focused on a specific area of machine learning.

The first course, “Machine Learning Foundations: A Case Study Approach,” covers the basics of machine learning, including data cleaning, regression, and classification. The second course, “Machine Learning: Regression,” focuses on the fundamentals of regression analysis and model selection.

The third course, “Machine Learning: Classification,” covers classification methods such as logistic regression, decision trees, and support vector machines. Finally, the fourth course, “Machine Learning: Clustering & Retrieval,” focuses on clustering algorithms and nearest neighbor search.

Throughout the program, students will work on several practical projects, including building a sentiment analysis system, predicting house prices, and implementing image classification algorithms.

The program is self-paced, meaning students can complete it on their own schedule, and it is designed for learners of all backgrounds, including those with no prior experience in programming or machine learning. Students receive feedback and support from industry professionals throughout the program, and upon completion, they receive a certificate that can be added to their resume or LinkedIn profile to showcase their skills in machine learning.

Overall, the Machine Learning Specialization offered by the University of Washington is an excellent choice for anyone looking to gain a comprehensive understanding of machine learning concepts and techniques. The program provides a solid foundation in the fundamentals of machine learning and offers hands-on experience building real-world projects, making it a valuable investment for aspiring data scientists and machine learning engineers.

• Analyze large and complex datasets, create systems that adapt and improve over time
• Learn Data Clustering and Classification algorithms
• Handle very large sets of features and select between models of various complexity
• Build intelligent applications that can make predictions from data
• Learn to deploy your solution as a service
• Practical case studies and programming assignments

Duration : 4 courses, Flexible Schedule, 6 hours per week
Rating : 4.8
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7. Mathematics for Machine Learning Specialization by Imperial College London (Coursera)

The Mathematics for Machine Learning Specialization offered by Imperial College London is an online program available on Coursera that provides students with a solid foundation in the mathematics necessary for machine learning. The program consists of three courses, each focused on a specific area of mathematics relevant to machine learning.

The first course, “Mathematics for Machine Learning: Linear Algebra,” covers the basics of linear algebra, including vectors, matrices, and matrix factorization. The second course, “Mathematics for Machine Learning: Multivariate Calculus,” covers multivariate calculus concepts such as partial derivatives, gradients, and optimization.

The third course, “Mathematics for Machine Learning: PCA,” focuses on principal component analysis, a widely used technique for dimensionality reduction and data compression in machine learning.

Throughout the program, students will work on several practical projects that apply the mathematical concepts learned in each course to real-world problems in machine learning.

The program is designed for learners of all backgrounds, including those with no prior experience in mathematics or machine learning. It is self-paced, meaning students can complete it on their own schedule. Students receive feedback and support from industry professionals throughout the program, and upon completion, they receive a certificate that can be added to their resume or LinkedIn profile to showcase their skills in the mathematical foundations of machine learning.

Overall, the Mathematics for Machine Learning Specialization offered by Imperial College London is an excellent choice for anyone looking to gain a solid foundation in the mathematics necessary for machine learning. The program provides a rigorous and comprehensive overview of linear algebra, multivariate calculus, and principal component analysis, making it a valuable investment for aspiring data scientists and machine learning engineers.

Key Highlights

• Gain prerequisite mathematical knowledge to pursue advanced courses in machine learning
• Graded assignments with peer feedback
• High school maths knowledge needed. Basic Python skills are an added advantage.
• Flexible schedule and self-paced learning

Duration : 3 courses, approx 2 months, 12 hours per week
Rating : 4.6
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8. Advanced Machine Learning Specialization by HSE (Coursera)

The Advanced Machine Learning Specialization offered by the Higher School of Economics (HSE) is an online program available on Coursera that provides students with advanced techniques and tools for machine learning. The program consists of six courses, each focused on a specific area of machine learning.

The first course, “Introduction to Deep Learning,” covers the basics of deep learning, including convolutional neural networks, recurrent neural networks, and generative models. The second course, “How to Win a Data Science Competition,” focuses on advanced techniques for data cleaning, feature engineering, and model selection.

The third course, “Bayesian Methods for Machine Learning,” covers the Bayesian approach to machine learning and its application to regression, classification, and clustering problems. The fourth course, “Practical Reinforcement Learning,” focuses on the practical implementation of reinforcement learning algorithms and their applications in robotics, gaming, and other fields.

The fifth course, “Deep Learning in Computer Vision,” focuses on deep learning techniques for computer vision tasks such as object detection, image segmentation, and image captioning. Finally, the sixth course, “Natural Language Processing,” covers advanced techniques for text classification, sentiment analysis, and language translation.

Throughout the program, students will work on several practical projects that apply the machine learning techniques learned in each course to real-world problems.

The program is designed for learners with a strong background in mathematics and programming, and it is recommended for professionals already working in the field of machine learning. Students receive feedback and support from industry professionals throughout the program, and upon completion, they receive a certificate that can be added to their resume or LinkedIn profile to showcase their advanced skills in machine learning.

Key Highlights

• Use modern deep neural networks for various machine learning problems with complex input
• Participate in data science competitions and use the most popular and effective machine learning tools
• Adopt the best practices of data exploration, preprocessing and feature engineering
• Perform Bayesian inference, understand Bayesian Neural Networks and Variational Autoencoders
• Use reinforcement learning methods to build agents for games and other environments
• Solve computer vision problems with a combination of deep models and classical computer vision algorithms
• Outline state-of-the-art techniques for natural language tasks, such as sentiment analysis, semantic slot filling, summarization, topics detection, and many others
• Build goal-oriented dialogue agents and train them to hold a human-like conversation
• Understand limitations of standard machine learning methods and design new algorithms for new tasks

Duration : 7 courses, Flexible Schedule
Rating : 4.6
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9. Deep Learning A-Z™: Hands-On Artificial Neural Networks (Udemy)

Deep Learning A-Z™: Hands-On Artificial Neural Networks is an online course available on Udemy that provides students with a comprehensive understanding of deep learning and artificial neural networks. The course is taught by two experts in the field of machine learning, Kirill Eremenko and Hadelin de Ponteves.

The course is divided into two parts, each focusing on a specific aspect of deep learning. The first part covers the basics of neural networks, including feedforward neural networks, convolutional neural networks, and recurrent neural networks. The second part focuses on advanced topics such as autoencoders, Boltzmann machines, and deep belief networks.

Throughout the course, students will work on several hands-on projects that apply the concepts learned in each section to real-world problems. These projects include image recognition, time series forecasting, and natural language processing.

The course is designed for learners of all backgrounds, including those with no prior experience in deep learning or neural networks. It is self-paced, meaning students can complete it on their own schedule. The course provides detailed explanations of each concept and includes practical coding exercises to ensure students have a strong grasp of the material.

Upon completion, students receive a certificate of completion that can be added to their resume or LinkedIn profile to showcase their skills in deep learning and artificial neural networks.

Overall, Deep Learning A-Z™: Hands-On Artificial Neural Networks is an excellent choice for anyone looking to gain a comprehensive understanding of deep learning and neural networks. The course provides a detailed and practical introduction to a wide range of topics in the field, making it a valuable investment for aspiring data scientists and machine learning engineers.

Key Highlights

• Work on real world datasets and design algorithms to solve real world challenges
• Learn the most popular open-source libraries Tensorflow and Pytorch and understand which one to use in certain circumstances
• Learn other libraries like Theano, Keras and Scikit-Learn
• Learn to evaluate the performance of our models (with most relevant technique, k-Fold Cross Validation) and improve them with effective Parameter Tuning
• Understand the intuition behind Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks
• Understand Self-Organizing Maps and apply them in practice
• Understand Boltzmann Machines and effectively apply them in practice
• Learn about Stacked autoencoders technique and how to use it
• Work on six real world case studies with updated datasets – Churn Modelling Problem, Image Recognition, Stock Price Prediction, Fraud Detection, Recommender Systems (like Amazon product suggestions and Netflix movie recommendations)

Duration : 22.5 hours on-demand video
Rating : 4.6
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10. Machine Learning with Python by IBM (Coursera)

Machine Learning with Python is an online course offered by IBM on the Coursera platform. The course provides an introduction to the fundamentals of machine learning using the Python programming language and popular machine learning libraries such as scikit-learn, pandas, and numpy.

The course is divided into four modules, each focused on a different aspect of machine learning. In the first module, students learn the basics of data analysis and preprocessing. The second module covers supervised learning techniques such as linear regression, logistic regression, and decision trees. The third module focuses on unsupervised learning techniques such as clustering and dimensionality reduction. In the final module, students learn how to evaluate and improve machine learning models.

Throughout the course, students work on hands-on projects that apply the concepts learned in each module to real-world problems. These projects include predicting housing prices, classifying customer behavior, and clustering similar articles.

The course is designed for learners with a basic understanding of Python programming and mathematics. It provides detailed explanations of each concept and includes practical coding exercises to ensure students have a strong grasp of the material.

Upon completion, students receive a certificate of completion that can be added to their resume or LinkedIn profile to showcase their skills in machine learning with Python.

Overall, Machine Learning with Python by IBM is an excellent choice for anyone looking to gain a solid foundation in machine learning using Python. The course provides a comprehensive introduction to a wide range of machine learning techniques and tools, making it a valuable investment for aspiring data scientists and machine learning engineers.

Key Highlights

• Learn to use various libraries to build machine learning models, like Scikit Learn
• Built-in lab environment (Jupyter notebook) with sample code
• Learn Regression, Classification, Clustering, Recommender Systems, SciPy
• Practice with different classification algorithms, such as KNN, Decision Trees,
  Logistic Regression and SVM
• Work on real world projects including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more

Duration : 5 to 6 weeks, 3 to 6 hours per week
Rating : 4.7
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