1- Free Deep Learning Course in Python

2- Machine Learning Nanodegree Program

3- Free Machine Learning Courses

4- Deep Learning Course by IBM

5- Machine Learning

6- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

7- Mathematics for Machine Learning: Linear Algebra

8- Structuring Machine Learning Projects

9- Google Cloud Platform Big Data and Machine Learning Fundamentals 

10- Machine Learning Foundations: A Case Study Approach

11- Neural Networks and Deep Learning

12- Deep Learning Specialization

13- Machine Learning A-Z™: Hands-On Python & R In Data Science

14- Deep Learning A-Z™: Hands-On Artificial Neural Networks

15- Unsupervised Machine Learning Hidden Markov Models in Python

16- Data Science: Supervised Machine Learning in Python

17- Machine Learning with Javascript

18- Data Science and Machine Learning Bootcamp with R

19- Deep Learning: Advanced Computer Vision 

20- Deep Learning Prerequisites: Linear Regression in Python

Course #1: Free Deep Learning Course in Python

Discover the wonders of machine learning with our in-depth Python course on deep learning! Learn how to use Keras 2.0, a free and easy-to-use Python library, to create and train neural network models under the supervision of Dan Becker, a former Google data scientist and Keras expert. In just four hours, you’ll gain a solid understanding of the basics of deep learning and neural networks, as well as the ability to apply techniques like backward propagation, a vital skill for improving your models. By following the Specify-Compile-Fit workflow, you’ll be able to design, tweak, and test various networks with different depths and widths. This course is more than just a learning experience; it’s an invitation to explore the endless possibilities of deep learning in fields like robotics, natural language processing, image recognition, and artificial intelligence. Don’t miss this chance to boost your Python skills, get a certification, and enter the exciting world of machine learning!

Offered by: DataCamp

Instructed by: Dan Becker (data scientist and contributor to Keras and TensorFlow libraries)

Price: free

Skills and Knowledge Imparted:

1. Understanding the significance of the techniques used in this area.
2. Building simple neural networks and generating predictions.
3. Using important methods to build deep learning models.

Difficulty Level: all levels

Duration and Total Lectures: 4 hours

Average User Rating: 4.4

Review:

Students appreciated Datacamp Courses, finding them immensely helpful in grasping the complexities of deep learning. The course stands out for its clear and concise explanations, providing a solid understanding before delving into coding. Many praise the instructor, Dr. Becker, for simplifying intricate concepts, making it easy to follow along, take notes, and practice with provided examples. Students appreciate the practical focus, interactive coding exercises, and the well-structured sequence that aids non-tech individuals in learning effectively. While some suggest improvements in mathematical expressions and the depth of certain topics, the majority laud this as one of the best deep learning courses, offering a strong foundation for further exploration in the field.

Course #2: Machine Learning Nanodegree Program

This course is built in collaboration with Kaggle and AWS (Amazon Web Services). Join this Python-based course on deep learning with PyTorch and discover how to build and train powerful models that can solve complex problems. You will understand the fundamentals of supervised and unsupervised learning, and learn how to use techniques such as linear regression, logistic regression, decision trees, Naive Bayes, support vector machines, neural networks, and clustering. You will also apply your skills to real-world projects, such as finding potential donors for a charity and segmenting customers based on their spending habits. You will use Python and PyTorch, two of the most popular tools for machine learning, and learn how to evaluate and tune your models. By completing this machine learning course, you will earn a certification that will showcase your achievement and boost your career prospects. Don’t miss this opportunity to grasp the power of machine learning with PyTorch! Enroll now and get started today!

Offered by: Udacity

Instructed by: Udacity online instructors

Price: free

Skills and Knowledge Imparted:

1. Machine learning basics.
2. Models in machine learning.
3. Supervised learning
4. Deep learning
5. Unsupervised learning

Difficulty Level: all levels

Duration and Total Lectures: 3 months

Average User Rating: 4.6

Review:

Students commend the course for its effective explanations, praising the clear instruction that aids understanding. Many find the projects well-designed, striking a balance between technical sufficiency and practical application. Some mention minor issues like missing instructor notes or ambiguous project tasks, but overall, the program is considered great. A learner appreciates the support team’s assistance in resolving technical problems, emphasizing the positive impact of prompt and efficient solutions. The program’s incorporation of both theory and hands-on coding exercises is lauded, providing a comprehensive learning experience that exceeds expectations. The engaging content and supportive environment make this course stand out among learners.

Related: Top 20 Python Courses, Tutorials and Certifications Online

Course #3: Free Machine Learning Courses

Start your amazing journey into the world of AI with one of the best web platforms that offer free machine learning courses and certifications. This course, taught by fast.ai online instructors, is suitable for both beginners and experienced learners, and provides a thorough and practical learning experience. The content covers machine learning fundamentals, models in machine learning, and explores deep learning, all with real-world examples and screenshots. What makes this platform unique is its dedication to offering deep learning courses for free, enabling you to learn concepts without any financial constraints. Using the fast.ai library, you’ll not only comprehend the details of AI but also train models effectively. Join the community forum to interact with other learners and experts, creating a cooperative learning environment. Don’t just learn about AI; understand, grasp, and certify your skills in machine learning with this powerful course.

Offered by: fast.ai

Instructed by: fast.ai online instructors

Price: free

Skills and Knowledge Imparted:

1. Machine learning basics.
2. Models in machine learning.
3. Deep learning

Difficulty Level: all levels

Duration and Total Lectures: self-paced

Average User Rating: 4.5

Review:

Fast.ai stands as a beacon in the realm of online learning, offering a robust platform for machine learning enthusiasts. While it doesn’t provide reviews about specific courses, the overall experience is noteworthy. With a focus on accessibility, their courses cover machine learning basics, deep learning, and more, all backed by hands-on examples. The platform’s commitment to making deep learning courses freely available sets it apart. Engaging with fast.ai allows learners to not just comprehend AI concepts but also cultivate practical skills. Joining their community forum enhances collaborative learning, making Fast.ai a valuable hub for those eager to delve into the world of AI.

Course #4: Deep Learning Course by IBM

Do you want to master the skills and techniques of deep learning, one of the most powerful fields of artificial intelligence? Join our hands-on artificial intelligence course and get a professional certificate in deep learning from IBM. You will learn the fundamental concepts of deep learning, including various neural networks for supervised and unsupervised learning. You will also build, train, and deploy different types of deep architectures, such as convolutional networks, recurrent networks, and autoencoders. You will use popular deep learning libraries such as Keras, PyTorch, and Tensorflow to apply deep learning to real-world scenarios, such as object recognition, computer vision, image and video processing, text analytics, natural language processing, recommender systems, and other types of classifiers. You will also master deep learning at scale with accelerated hardware and GPUs. This course is self-paced and includes six skill-building courses and a capstone project. By completing this course, you will not only understand and grasp the power of deep learning, but also certify your skills and boost your career prospects. Don’t wait, enroll now!

Offered by: IBM via edX

Instructed by: Joseph Santarcangelo and Saeed Aghabozorgi (data science faculty).

Price: $485

Skills and Knowledge Imparted:

1. Fundamental concepts of deep learning. Such as various neural networks for supervised and unsupervised learning.
2. Using popular deep learning libraries to apply them to industry problems.
3. Building, training and deploying different types of deep architectures.
4. Application of deep learning to real-world scenarios.

Difficulty Level: all level

Duration and Total Lectures: 2-4 months

Average User Rating: 4.6

Review:

EDX Courses stand as a diverse and reputable platform for online learning, offering a wide array of subjects, including the coveted field of artificial intelligence. However, it’s crucial to note that EDX doesn’t furnish reviews for specific courses. Despite this, the platform provides valuable educational content, user-friendly interfaces, and courses from renowned institutions like IBM. The Professional Certificate in Deep Learning exemplifies EDX’s commitment to hands-on learning. Learners can explore a range of topics, though specific course reviews may be unavailable. EDX remains a solid choice for those seeking quality education from prestigious institutions.

Course #5: Machine Learning

This online machine learning course offers comprehensive insights into the most advanced AI systems and practical guidance on applying them effectively. You’ll gain a deep understanding of both theoretical concepts and essential skills needed to address real-world challenges. The course covers key topics such as:

1. Supervised Learning: Explore parametric and non-parametric algorithms, support vector machines, kernels, and neural networks.
2. Unsupervised Learning: Learn about clustering, dimensionality reduction, recommender systems, and deep learning.
3. Best Practices in AI: Understand critical concepts like bias/variance trade-offs and industry-proven development methodologies.

Through a combination of case studies and applications, you’ll learn how to apply machine learning algorithms to create intelligent robots, enhance computer vision, and improve medical informatics. Taught by Andrew Ng, a renowned expert in AI, this Stanford University course on Coursera provides valuable insights into the latest AI technologies and their practical applications. Plus, the course is free, offering an excellent opportunity to gain expertise in artificial neural networks and machine learning algorithms from one of the field’s leading innovators.

Offered by: Stanford University via Coursera

Instructed by: Andrew Ng (CEO of Landing AI. Co-founder of Coursera. Adjunct professor at Stanford University. Former chief scientist at Baidu. And founding lead of Google Brain).

Price: free

Skills and Knowledge Imparted:

1. Artificial neural networks
2. And machine learning algorithms.

Difficulty Level: all levels

Duration and Total Lectures: 56 hours

Average User Rating: 4.9 (118,348 ratings)

Review:

Students unanimously praise the machine learning course, lauding its effectiveness for those already versed in mathematics and Python. The course stands out for its clarity in explaining complex ideas, making it a valuable experience. The practical labs, though optional, provide hands-on coding experience, reinforcing theoretical concepts. However, some students desire more in-depth coverage of mathematical aspects and find certain assignments lacking engagement. Despite these nuances, the course receives widespread acclaim for its concise, up-to-the-point delivery and the seamless transition from theory to practical implementation. With Andrew Ng’s adept teaching style, this beginner-friendly course offers a comprehensive foundation in machine learning, making it a top choice for aspiring learners.

Course #6: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Join DeepLearning.AI’s courses to dive into the dynamic world of machine learning, a cornerstone of computer science. Tailored for enthusiasts and developers, this comprehensive curriculum covers fundamental concepts and offers hands-on Python training, ensuring you can apply your knowledge to real-world challenges.

For those aiming to develop scalable AI-powered algorithms, the Machine Learning in TensorFlow Specialization, led by Andrew Ng, provides in-depth instruction on using TensorFlow effectively. You’ll explore essential principles of machine learning and deep learning, gaining practical skills to tackle real-world problems.

These courses offer free, high-quality education and the opportunity to earn valuable certifications in Python and machine learning. Whether you’re a budding developer or a seasoned professional, this is your chance to enhance your skills and showcase your expertise in these in-demand fields. Don’t miss out on this opportunity to elevate your career in the ever-evolving domain of machine learning.

Offered by: deeplearning.ai via Coursera

Instructed by: Laurence Moroney (advocate of artificial intelligence at Google Brain).

Price: free

Skills and Knowledge Imparted:

1. Computer vision
2. And machine learning.

Difficulty Level: intermediate

Duration and Total Lectures: 9 hours

Average User Rating: 4.7 (4,203 ratings)

Review:

Learners found the introductory course on Convolutional Neural Networks in Keras helpful for building image classifiers. They appreciated the practical approach and the instructor’s ability to deliver knowledge effectively. The course was praised for demystifying computer vision use-cases and serving as a great follow-on to Andrew Ng’s Machine Learning course. However, some students pointed out issues with Google Colab assignments and suggested improvements.  

 The course’s hands-on approach, byte-sized format, and problem-based learning received positive feedback, making it a worthwhile investment for those new to AI. 

Course #7: Mathematics for Machine Learning: Linear Algebra

Mastering machine learning requires a strong foundation in linear algebra, and that’s exactly what this course, Mathematics for Machine Learning: Linear Algebra, offers. As part of the Mathematics for Machine Learning Specialization, this course is designed to equip you with essential skills and techniques. You will dive into key concepts such as vectors, matrices, eigenvalues, and eigenvectors, learning both the theory and practical applications.

Through engaging examples like rotating images and analyzing the Pagerank algorithm, you’ll see how these mathematical concepts are applied to real-world problems. You’ll also get hands-on experience by implementing these ideas in Python using Jupyter notebooks. By the end of this course, you’ll have an intuitive understanding of linear algebra and be ready to apply these skills to machine learning challenges.

Don’t miss the opportunity to build your expertise in this crucial area. Enroll now and take the first step towards mastering the mathematical foundations of machine learning.

Offered by: Imperial College London via Coursera

Instructed by: David Dye, Samuel J. Cooper and A. Freddie Page (engineering faculty).

Price: free

Skills and Knowledge Imparted:

1. Eigenvalues and Eigenvectors
2. Linear algebra
3. Transformation matrix

Difficulty Level: beginner

Duration and Total Lectures: 22 hours

Average User Rating: 4.7 (4,103 ratings)

Review:

Students found the course highly beneficial. They particularly appreciated the emphasis on developing an intuitive grasp of linear algebra concepts. Real-world Python exercises were highlighted as valuable for enhancing practical skills and making the content more applicable. The clear and accessible language used by the lecturers was a significant plus, especially for non-native English speakers. Many students praised the course’s well-structured pace and comprehensive scope. However, some pointed out drawbacks, such as limited coding guidance and a lack of responsiveness from the course instructors. Despite these mixed reviews, the course was widely recognized for effectively building a solid foundation in linear algebra, boosting students’ confidence in their machine learning endeavors.

Related: 6 Best & Free Certificate Courses Online.

Course #8: Structuring Machine Learning Projects

This course will teach you how to build successful AI projects and lead your team effectively. You’ll learn decision-making skills and strategies to optimize machine learning workflows, drawn from Andrew Ng’s extensive experience in deep learning. The course includes two pilot training programs for practical leadership experience, typically gained only through years in the industry.

As part of the Deep Learning Specialization, you’ll cover key concepts like error diagnosis, strategy prioritization, end-to-end learning, transfer learning, and multi-task learning. Ideal for those with basic machine learning knowledge, this course helps elevate your skills and provides a shareable certificate to showcase your expertise. Enroll now to gain valuable insights and practical experience in AI project leadership.

Offered by: deeplearning.ai via Coursera

Instructed by: Andrew Ng. Teaching assistant, Younes Bensouda Mourri. And teaching assistant head, Kian Katanforoosh (Stanford faculty).

Price: free

Skills and Knowledge Imparted:

1. Machine learning
2. Deep learning
3. Inductive transfer

Difficulty Level: beginner

Duration and Total Lectures: 7 hours

Average User Rating: 4.8 (32,560 ratings)

Review:

Students across various backgrounds found this course immensely helpful in providing practical insights and strategies for machine learning (ML) projects. The course goes beyond technical details, offering a high-level view on directing efforts in ML projects. Learners appreciated the enjoyable and useful content, highlighting its real-world relevance. The course stands out for its focus on practical advice and analysis techniques, making it a crucial part of the ML/deep learning toolbox. While some wished for hands-on coding assignments, the challenging “flight simulator” quizzes compensated for the absence of projects. Overall, students recognized the unique value of the course in guiding them through the intricacies of ML projects.

Course #9: Google Cloud Platform Big Data and Machine Learning Fundamentals

Learn how to harness the power of big data and machine learning on Google Cloud with the Google Cloud Big Data and Machine Learning Fundamentals course, a key step towards Google Certifications. This course, taught in English by Google Cloud Training experts, covers the data-to-AI lifecycle on Google Cloud, using TensorFlow, BigQuery, and Google Cloud Platform. You will learn how to create streaming pipelines with Dataflow and Pub/Sub, and how to use Vertex AI and AutoML to build machine learning solutions. This 9-hour course, available through various programs and with financial aid options, is suitable for beginners and offers a flexible schedule. You will also earn a shareable career certificate to boost your LinkedIn profile and resume. 

But before selecting this course, members ought to have around one year of involvement in at least one of the accompanying. A typical inquiry language, for example, SQL; extract, change, load exercises; data demonstrating and more. Join over 300,000 learners who have already taken this course, and gain in-demand skills that will open up new opportunities in the field of big data and machine learning. Don’t wait, enroll now and discover the secrets of big data and machine learning on Google Cloud.

Offered by: Google Cloud via Coursera

Instructed by: Google Cloud Training

Price: free

Skills and Knowledge Imparted:

1. Tensorflow
2. Bigquery
3. Google Cloud Platform
4. And cloud computing.

Difficulty Level: intermediate

Duration and Total Lectures: 13 hours

Average User Rating: 4.6 (7,549 ratings)

Review:

Participants praise the Google Cloud Big Data and Machine Learning Fundamentals course for its practical application of machine learning on the Google Cloud Platform (GCP). Transitioning from theoretical knowledge, they find the course remarkably helpful in demonstrating how easy it is to implement machine learning using GCP tools like TensorFlow and BigQuery. The passionate instructors and comprehensive content foster an understanding of the entire data-to-AI lifecycle. Although some find certain segments lengthy, the inclusion of Qwiklabs and Datalab notebooks provides valuable hands-on experience. Students appreciate the course’s focus on GCP integration, making it stand out as a practical and informative choice among machine learning certifications. Despite occasional technical glitches, participants express gratitude to Google and Coursera for this transformative learning experience.

Course #10: Machine Learning Foundations: A Case Study Approach

Discover the world of machine learning with the “Machine Learning Foundations: A Case Study Approach” course, an essential part of the Machine Learning Specialization. This course is ideal for beginners who want to learn by doing. You’ll work on real-world case studies that reveal the potential of machine learning and its diverse applications. You’ll be guided by experienced instructors Emily Fox and Carlos Guestrin, who will teach you how to predict house prices, analyze sentiment from texts, retrieve documents, build recommender systems, and use deep learning for image search. The course will not only demystify the black box of machine learning but also help you understand the tasks, choose the right tools, and evaluate the results. With a flexible schedule, interactive projects, and a shareable career certificate, this course will prepare you to apply machine learning in different domains. Join the learners who have already signed up and boost your skills in Python programming, machine learning concepts, and deep learning. Don’t let this opportunity pass you by and earn a valuable credential for your LinkedIn profile and a solid foundation in this exciting technology.

Offered by: University of Washington via Coursera

Instructed by: Carlos Guestrin and Emily Fox (machine learning professors).

Price: free

Skills and Knowledge Imparted:

1. Python programming
2. Machine learning concepts
3. Deep learning

Difficulty Level: all levels

Duration and Total Lectures: 24 hours

Average User Rating: 4.6 (9,003 ratings)

Review:

Learners praise the machine learning course for its unique approach to teaching core concepts using real-life examples. The instructors, Emily and Carlos, receive positive feedback for their engaging teaching style, incorporating case studies that effectively help students understand machine learning. The course stands out for its hands-on sessions, making it accessible to beginners. While some mention concerns about the use of the GraphLab library, the majority appreciate the practical, problem-solving focus of the course. Despite some technical challenges, students find it informative, well-structured, and a valuable introduction to machine learning, showcasing its application in various domains.

Course #11: Neural Networks and Deep Learning

In this course, you will gain proficiency with the establishments of profound learning. At the point when you finish this class, you will understand the significant innovation patterns driving Deep Learning be ready to fabricate, train and apply completely associated profound neural systems; know how to actualize proficient (vectorized) neural systems and understand the key parameters in a neural system’s engineering.

This course shows you how Deep Learning really functions, as opposed to introducing just a surface-level portrayal. So in the wake of finishing it, you will have the option to apply profound figuring out how to a your very own applications. In the event that you are searching for a vocation in AI, after this course you will likewise have the option to respond to fundamental inquiries questions. This is the primary course of the Deep Learning Specialization.

Offered by: deeplearning.ai via Coursera

Instructed by:

Andrew Ng. Teaching assistant, Younes Bensouda Mourri. And teaching assistant head, Kian Katanforoosh (Stanford faculty).

Price: free

Skills and Knowledge Imparted:

1. Artificial neural networks
2. Python programming
3. Deep learning

Difficulty Level: intermediate

Duration and Total Lectures: 18 hours

Average User Rating: 4.9 (62,957 ratings)

Review:

Students found this course on Neural Networks and Deep Learning to be exceptionally helpful, praising its detailed explanations and practical assignments. The content goes beyond Andrew Ng’s original Machine Learning course, providing a deeper dive into neural networks using Python. While some students expressed concerns about the assignments being too simple, many appreciated the step-by-step implementation of artificial neural network algorithms. The course received accolades for breaking down complex concepts into understandable segments, making it suitable for learners from various backgrounds. The instructor, Andrew Ng, was lauded for his engaging teaching style and the course’s overall structure. Students highlighted the value of gaining a solid understanding of the mathematics behind deep learning and the ability to implement algorithms from scratch. The majority found the course highly beneficial for building a foundational understanding of neural networks.

Course #12: Deep Learning Specialization

Learn from the best and become an expert in artificial intelligence with the Deep Learning certification, led by renowned instructors including Andrew Ng, Younes Bensouda Mourri, and Kian Katanforoosh. This updated program covers the latest techniques and tools for building and training neural networks. You will learn the fundamentals of deep learning and explore its diverse applications, from speech recognition to music synthesis. You will also use TensorFlow, the most popular framework for deep learning, to create and optimize your own models. The courses are intermediate-level and self-paced, giving you the flexibility to learn according to your schedule. By the end of this program, you will earn a shareable certificate that showcases your skills in Tensorflow, deep learning, hyperparameter tuning, mathematical optimization, and more. This specialization is your gateway to a successful career in AI. Don’t wait and enroll today to gain hands-on experience, receive career guidance, and boost your subject-matter expertise in this exciting domain.

Offered by: deeplearning.ai via Coursera

Instructed by:

Andrew Ng. Teaching assistant, Younes Bensouda Mourri. And teaching assistant head, Kian Katanforoosh (Stanford faculty).

Price: free

Skills and Knowledge Imparted:

1. Tensorflow
2. Convolution neural networks
3. Artificial neural networks
4. Deep learning

Difficulty Level: intermediate

Duration and Total Lectures: 3 months

Average User Rating: 4.8 (69,745 ratings)

Review:

Students have lauded the Deep Learning Specialization for its clear and comprehensive explanation of underlying concepts. Even for those acquainted with deep neural networks, it serves as an invaluable complementary course, addressing any missed pieces. The structured material, starting from logistic regression and progressing through various layers, provides a fantastic introduction. Dr. Andrew Ng’s teaching prowess shines through, making complex mathematical concepts accessible. The course stands out for its simplicity in Python coding, well-designed assignments, and the incorporation of real-world applications. Despite varied opinions, the majority praises the lecturer for instilling confidence and providing a solid foundation in machine learning engineering. The course’s impact extends beyond expectations, earning it a solid recommendation for anyone venturing into data science or machine learning.

Course #13: Machine Learning A-Z™: Hands-On Python & R In Data Science

If you want to master machine learning on Python and R, then this course is for you. This course is designed by two expert data scientists who will share their knowledge and help you understand complex theories, algorithms, and coding libraries in a simple way. They will walk you step by step into the world of machine learning, where you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of data science. You will also get hands-on practice building your own models using real-world examples. Plus, you will get access to both Python and R code templates that you can download and use on your own projects. This course covers specific topics like reinforcement learning, natural language processing, and deep learning, as well as advanced techniques like dimensionality reduction. You will learn how to choose the right machine learning model for each problem and how to combine them to solve any problem. By the end of this course, you will have a great intuition of many machine learning models, make accurate predictions, make powerful analyses, create robust machine learning models, create strong added value to your business, and use machine learning for personal purpose. This course is offered by Udemy and taught by Kirill Eremenko, Hadelin de Ponteves, and is a bestseller and a must-have for anyone who wants to embark on a machine learning journey. Don’t miss this opportunity and enroll now!

Offered by: Udemy

Instructed by: Kirill Eremenko (Data Scientist). Hadelin de Ponteves (AI Entrepreneur). And Super Data Science Team.

Price: $19.99

Skills and Knowledge Imparted:

1. Having a great intuition of machine learning models.
2. Making accurate analyses and robust machine learning models.
3. Adding value to your business.
4. Using machine learning for personal use.
5. Handling specific topics like natural language processing.
6. Knowing which machine learning model to choose for each type of problem.
7. Building an army of powerful machine learning models.
8. And how to combine them to solve any problem.

Difficulty Level: all levels

Duration and Total Lectures: 294 lectures, 41 hours

Average User Rating: 4.5 (96,439 ratings)

Review:

Students found this Machine Learning course to be remarkably helpful and distinct. The instructors excelled in explaining the intuition behind various ML tools, contributing to its appeal. The course’s structure received acclaim, providing both beginners and experienced professionals with a valuable overview of different ML areas. The enthusiasm displayed by the instructors resonated well with learners, fostering a positive learning environment. Practical tutorials and hands-on exercises were highlighted as particularly beneficial, aiding in grasping complex concepts. While some students suggested improvements, such as separating Python and R into distinct courses, the majority praised the course’s effectiveness in building a strong foundation in machine learning.

Course #14: Deep Learning A-Z™: Hands-On Artificial Neural Networks

If you want to create deep learning models in Python, then this course is for you. This course is taught by two machine learning and data science experts who will help you grasp the intuition and the application of various neural network architectures. You will embark on a journey into the world of artificial intelligence, where you will explore Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Self-Organizing Maps (SOMs), Boltzmann Machines (BMs), and AutoEncoders (AEs). You will also get hands-on practice using code templates that you can download and use on your own projects. This course covers both the theory and the practice of deep learning, giving you the skills to build and train powerful models for various tasks. By the end of this course, you will understand the potential of deep learning and be ready to apply it to your own problems. Plus, you will have a chance to win the ChatGPT Prize, a competition that challenges you to create a chatbot using deep learning. Don’t miss this opportunity and enroll now!

Offered by: Udemy

Instructed by: Kirill Eremenko (Data Scientist). Hadelin de Ponteves (AI Entrepreneur). And Super Data Science Team.

Price: $10.44

Skills and Knowledge Imparted:

1. Understanding the basics of different neural networks.
2. Practicing with them.
3. And using them to solve different problems in the technological world.

Difficulty Level: all levels

Duration and Total Lectures: 188 lectures, 23 hours

Average User Rating: 4.5 (24,840 ratings)

Review:

Students found the course helpful with clear explanations of complex topics in simple terms. They appreciated detailed explanations, practical sessions, and valuable references. The course stands out for its up-to-date content, making it relevant for learners. Some students desired more technical details, while others praised the instructor’s enthusiasm and intuitive explanations. The course received positive feedback for covering a variety of deep learning topics. However, a few noted outdated code and suggested improvements in pacing and content organization. Despite varied opinions, the majority found the course beneficial for gaining both theoretical and practical knowledge in deep learning.

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Course #15: Unsupervised Machine Learning Hidden Markov Models in Python

If you’re interested in unsupervised machine learning and modeling sequences of data, this course is tailored for you. It delves into the probability distribution of sequences of random variables using Markov models and hidden Markov models (HMMs). You’ll learn to optimize model parameters through gradient descent, a versatile technique also integral to deep learning, as an alternative to the expectation-maximization algorithm. The course leverages popular libraries like Theano and TensorFlow to work with sequences, guiding you to build and understand models from scratch.

Beyond the technical foundation, the course explores practical applications of Markov models and HMMs, such as predicting health outcomes, analyzing website behavior to address high bounce rates, constructing language models for author identification and content generation, and understanding Google’s PageRank algorithm. You’ll use downloadable code templates for hands-on practice, gaining intuitive insights through model visualization. This course not only teaches you how to use these models but also equips you to understand and create them independently. Enroll now to deepen your expertise in unsupervised machine learning and sequence modeling.

Offered by: Udemy

Instructed by: Lazy Programmer Inc. (machine learning engineer)

Price: $10.44

Skills and Knowledge Imparted:

1. Understanding and listing the various applications of machine learning models.
2. Writing the code for some common machine learning models.
3. And then applying them to any sequence of data.
4. Understanding how Google’s Page Rank works.
5. And how gradient descent, which is normally used in deep learning, can be used machine learning models.

Difficulty Level: all levels

Duration and Total Lectures: 62 lectures, 9 hours

Average User Rating: 4.5 (1,905 ratings)

Review:

Students universally praise Lazy Programmer’s Hidden Markov Models (HMM) course for its in-depth explanations and practical applications. The course stands out for striking the right balance between theory and real-world applications. The instructor, referred to as Lazy Programmer, is recognized for providing deep insights into HMM, making complex concepts clearer. The course covers basics thoroughly and advances into intricate details, preparing students to apply HMM effectively. The mix of theory, coding, and practical examples received positive feedback. While some students found certain sections challenging, the majority appreciate the comprehensive content and the instructor’s up-to-date approach in the field of machine learning. Lazy Programmer’s commitment to responding promptly to queries and providing additional resources contributes to the overall positive learning experience. Students recommend the course for those seeking a deep understanding of HMM and its practical implementations.

Course #16: Data Science: Supervised Machine Learning in Python

This course provides a comprehensive introduction to supervised machine learning, focusing on key algorithms such as K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, and Perceptron. KNN is particularly highlighted for its simplicity and intuitive nature, making it an excellent starting point for beginners, though the course also delves into its limitations. Students will also explore how to convert the Bayes Classifier into both linear and quadratic classifiers to optimize computations. Perceptrons, being the foundation of neural networks and deep learning, are examined closely, with detailed discussions on the pros and cons of each algorithm. The course emphasizes understanding the strengths and weaknesses of each approach, fostering a deep understanding of machine learning techniques. Alongside theory, practical skills are developed through coding exercises using Python, Numpy, and Pandas, and optimizing models with the Sci-Kit Learn library. Students will learn to fine-tune their models through hyperparameter tuning, cross-validation, and feature extraction. The course also compares traditional machine learning with deep learning, shedding light on the differences and trade-offs between the two. To solidify learning, the course concludes with a hands-on project where students create a machine learning web service, addressing real-world challenges such as medical image analysis and self-driving cars. Packed with practical exercises, quizzes, and code templates, this course is a perfect starting point for anyone eager to dive into data science and machine learning, whether for career advancement or personal projects.

Offered by: Udemy

Instructed by: Lazy Programmer Inc. (Machine Learning engineer)

Price: $10.44

Skills and Knowledge Imparted:

1. Working with Python programming language.
2. And understanding and implementing basics tasks in it.
3. The concepts of feature extraction and feature selection.
4. Understanding the pros and cons between classic machine learning methods and deep learning.
5. Implementing a machine learning web service.

Difficulty Level: all levels

Duration and Total Lectures: 52 lectures, 6 hours

Average User Rating: 4.6 (1,308 ratings)

Review:

Students found the course valuabel for them. Encompassing a detailed exploration of all machine learning algorithms, the course is lauded for its meticulous organization. For many, it has proven instrumental in solidifying foundational concepts, offering clear examples and real-world use cases. The instructor’s approach, emphasizing a bottom-up learning style and steering clear of mere API usage, resonates well with learners. Despite varied experiences, the majority express appreciation for the course’s depth, explaining complex theories concisely. While some note minor issues, like the expectation for self-setup, the overall consensus is that this course stands out in the realm of machine learning education, providing an indispensable foundation for aspiring data scientists.

Course #17: Machine Learning with Javascript

There are many online courses available on machine learning, but this course stands out as the best introduction to the topic. It covers every key concept and even includes JavaScript. Machine learning with JavaScript is easier to learn than with Python. While Python is popular, it is often seen as an “expressive” language, meaning it can be confusing for beginners. Most online courses focus on the complicated math behind machine learning, but this course simplifies that. The goal is for students to understand both the math and the programming techniques used in common ML algorithms.

If you’re interested in machine learning and want to create your own algorithms using JavaScript and TensorFlowJS, this course is perfect for you. Taught by an expert, it helps you understand the math and code behind machine learning techniques. You will learn how to build applications using JavaScript and optimize algorithms using TensorFlowJS. Additionally, you will explore low-level features to enhance performance and memory usage. The course also teaches how to plot results with a custom-built graphing library. By the end, you’ll be able to write clear and efficient ML code, without relying on complex libraries. Plus, you’ll earn a JavaScript certification to add to your resume or LinkedIn profile. This course is a must for anyone wanting to master machine learning with JavaScript and TensorFlowJS. Enroll now!

Offered by: Udemy

Instructed by: Stephen Grider (engineering architect)

Price: $10.44

Skills and Knowledge Imparted:

1. Assembling machine learning algorithms from scratch.
2. Building interesting applications.
3. Understanding how machine learning works without relying on mysterious libraries.
4. Optimizing algorithms in your machine learning models for better performance.
5. Growing a strong intuition of best practices in machine learning.

Difficulty Level: intermediate

Duration and Total Lectures: 185 lectures, 17.5 hours

Average User Rating: 4.6 (1,421 ratings)

Review:

This machine learning course has garnered praise from students for its exceptional clarity in explaining complex concepts. Unlike other courses that skip essential steps, this one, led by Stephen Grider, adeptly breaks down major ML ideas, making them accessible for learners. The engaging and comprehensible video content is appreciated, with students expressing gratitude for a well-structured and informative curriculum. Grider’s teaching style, which delves deeply into fundamental concepts, stands out, ensuring a thorough understanding of each topic. While a few noted outdated syntax and unfulfilled promises, the course’s overall value, especially in demystifying intricate mathematics using JavaScript, is acknowledged. The majority find it a worthwhile investment of time and money, recommending it as a fantastic introduction to machine learning.

Course #18: Data Science and Machine Learning Bootcamp with R

If you’re passionate about data science and want to master the R programming language for machine learning and data visualization, this course is perfect for you. Taught by an expert, it covers a wide range of machine learning algorithms and data analysis techniques using R. You will learn to manipulate data with R data frames, handle various data formats, perform web scraping, and connect to SQL databases. The course also teaches you how to create stunning visualizations with ggplot2 and plotly.

Explore key machine learning methods such as linear regression, k-means clustering, decision trees, random forests, neural nets, and support vector machines. Apply these concepts to real-world scenarios, like data mining on Twitter. The course blends theory and practical application, ensuring you gain proficiency in using R for data science and machine learning.

This course is designed for beginners with no programming experience and experienced developers wanting to transition into data science. It offers the same valuable knowledge as costly bootcamps but at a fraction of the price. With over 100 HD video lectures and detailed code notebooks, this is one of the most comprehensive courses on data science and machine learning available. Enroll now and gain a certification that will boost your resume and LinkedIn profile.

Offered by: Udemy

Instructed by: Jose Portilla (head of data science)

Price: $10.44

Skills and Knowledge Imparted:

1. Programming in R.
2. Using it for analyzing and manipulating data.
3. Creating data visualizations.
4. And using it for machine learning algorithms as well as data science.

Difficulty Level: all levels

Duration and Total Lectures: 127 lectures, 17.5 hours

Average User Rating: 4.6 (8,581 ratings)

Review:

Students across various backgrounds found this course instrumental in unlocking opportunities in data analysis. While opinions on the course were mixed, the dual structure focusing on R basics for data science and subsequent machine learning garnered positive feedback. The instructor’s depth of explanation, coupled with practical tips on self-researching functions, resonated well. Projects in the ML sections proved pivotal for testing comprehension. Some constructive criticism surfaced regarding the initial shallowness, especially in SQL and regular expressions. The instructor’s dedication, clear narration, and comprehensive coverage of R basics and machine learning techniques distinguished the course. Some students wished for more exercises, theoretical tests, and updated material, but the majority considered it a worthwhile investment. Overall, the course was acknowledged for its practicality, comprehensive content, and the instructor’s effective teaching style.

Course # 19: Deep Learning: Advanced Computer Vision

In this course, you will learn to transform a CNN into an object recognition system. This system not only classifies images but also detects objects and predicts their names, a critical task for self-driving vehicles. It needs to recognize cars, pedestrians, bikes, traffic lights, and more in real-time.

The course covers an advanced algorithm called SSD, which is faster and more accurate than its predecessors. You will also explore neural style transfer, where you combine a content image with a style image to create a new one. The course moves beyond CNNs to systems that include CNNs, with a focus on practical applications.

Designed for those passionate about computer vision, this course teaches deep learning techniques using Python. You’ll study CNN architectures like VGG, ResNet, and Inception, along with object detection algorithms such as SSD. Key topics include class activation maps, generative adversarial networks, and object localization. The course also covers AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion.

You will build and train models with Tensorflow, Keras, and Python, applying both high-level and low-level code for deep learning applications. This course ensures you gain a deep understanding of computer vision and AI technologies. Enroll now to enhance your skills in this cutting-edge field!

Offered by: Udemy

Instructed by: Lazy Programmer Inc. (machine learning engineer)

Price: $10.44

Skills and Knowledge Imparted:

1. Understanding and applying transfer learning.
2. Using state-of-the-art convolution neural nets.
3. Understanding and using object detection algorithms.
4. And applying neural style transfer.
5. Understanding state-of-the-art computer topics.

Difficulty Level: advanced

Duration and Total Lectures: 68 lectures, 7 hours

Average User Rating: 4.7 (1,821 ratings)

Review:

Participants praise the course, giving it an average rating of 4.6 out of 5. The reviews highlight the detailed explanations, practical examples, and a well-balanced approach. Many appreciate the use of images aiding comprehension and the step-by-step teaching style. The course stands out for its simplicity, making complex topics easy to grasp. Some students express gratitude for the instructor’s honesty and emphasis on hard work. A few mention the course’s depth, comparing it favorably to other machine learning courses. Despite one feedback suggesting a lack of advanced content, the majority finds the course highly valuable for beginners and experienced learners alike.

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Course #20: Deep Learning Prerequisites: Linear Regression in Python

If you want to start learning data science, machine learning, and artificial intelligence, the Deep Learning Prerequisites: Linear Regression in Python course is a great place to begin. Linear regression is one of the simplest AI models, yet it’s used repeatedly in many fields. This course teaches you how to create and solve linear regression models in Python, using free tools like Python and its libraries.

You’ll begin with 1-D linear regression and apply it to demonstrate Moore’s Law. Then, you’ll learn how to expand this to multi-dimensional linear regression, enabling you to predict real-world outcomes, like a patient’s systolic blood pressure based on their age and weight. The course also covers regularization and numerical methods to improve your models.

You’ll practice on real data, gaining hands-on experience and seeing the impact of your work. The course also introduces advanced topics, such as OpenAI’s ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion, and shows how linear regression can lay the groundwork for understanding these technologies. Whether you’re a developer looking to expand your coding skills or someone with a technical background wanting to apply your expertise in data science, this course will help you build a solid foundation. Enroll now and discover the power of linear regression.

Offered by: Udemy

Instructed by: Lazy Programmer Inc. (machine learning engineer)

Price: $10.44

Skills and Knowledge Imparted:

1. Deriving and solving a linear regression model.
2. Applying it appropriately to data science problems.
3. Programming your own version of a linear regression model in Python.

Difficulty Level: all levels

Duration and Total Lectures: 50 lectures, 6 hours

Average User Rating: 4.6 (3,560 ratings)

Review:

The instructor is clear and concise, making the course easy to follow. Students appreciate the balance between theory and implementation, with no shortcuts in the math. The course offers strong coverage of mathematical derivations and practical Python coding, ensuring both beginners and advanced learners gain valuable skills. Despite some concerns about advanced math, the well-structured resources and the instructor’s challenging approach are widely praised. Students enjoy the focus on practical applications and the encouragement to solve problems independently. Overall, the course is highly recommended for those wanting a deep understanding of linear regression, gradient descent, and machine learning.