20 Machine Learning Courses and Certifications

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

20 Machine Learning Courses and Certifications

Course #1: Free Deep Learning Course in Python

Profound learning is the AI strategy behind the most energizing abilities in different zones. Such as common language handling, image recognition, and AI, including the well-known AlphaGo. Thus in this Machine Learning course, learners will get addition hands-on, down to earth information of how to utilize profound learning with Keras 2.0, the most recent adaptation of a front line library for profound learning in Python. Furthermore, this machine learning course tracks include Data Scientist with Python; Machine Learning with Python and Machine Learning Scientist with Python.

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:

Learning the fundamentals of neural networks and how to build deep learning models using Keras 2.0 becomes very easy through this course. But Iits prerequisites include an introductory understanding of Python and supervised learning with scikit-learn. To conclude, DataCamp is the top resource users recommend for learning data science.

Course #2: Machine Learning Nanodegree Program

This course is built in collaboration with Kaggle and AWS (Amazon Web Services). So, get to learn primary AI calculations, beginning with information cleaning and managed models. In fact, at that point, proceed onward to investigating profound and unaided learning. At each progression, get handy experience by applying your aptitudes to code activities and ventures. Also, this program is purposed at students with involvement with Python, who have not yet contemplated Machine Learning subjects.

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:

Interactive quizzes allow you to brush up the topics covered. And you get to join the student support community to exchange ideas and clarify doubts. Furthermore, the self-paced schedules allow you to learn at your convenience. And you also get a one-on-one mentor, personal career coaching along with access to the student community. To conclude, this is an amazing course accompanied by great content.

Related: Top 20 Python Courses, Tutorials and Certifications Online

Course #3: Free Machine Learning Courses

This is one of the top web platforms that give seminars on subjects that go under AI. And it is made with the intent to encourage the majority about AI and how to begin in the field. So, all the substance is secured without any preparation and focuses on learning by doing. There are progressions of decisions accessible for the two apprentices and experienced students. All the deep learning courses on this platform are available freely. And by using the fast.ai library, you can train models even!

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:

Every concept is covered with screenshots and hands-on examples. Complete guidance is provided to perform the configuration to get started with the lectures. Join the forum to communicate with peers and practitioners and help each other through the learning experience.

Course #4: Deep Learning Course by IBM

From these courses, you’ll be acquainted with ideas and applications in Deep Learning, including different sorts of Neural Networks for regulated and unaided learning. Then you’ll dive further and apply Deep Learning by building models and calculations utilizing libraries like Keras, PyTorch, and Tensorflow. Similarly, you’ll ace Deep Learning at scale by utilizing GPU quickened equipment for picture and video preparing, just as article acknowledgment in Computer Vision.

And all through this Machine Learning program, you will rehearse your Deep Learning abilities through a progression of hands-on labs, assignments. As well as tasks motivated by certifiable issues and informational indexes from the business. Additionally, you will get to finish the program by setting up a Deep Learning capstone venture. And that will feature your applied abilities to imminent businesses.

Offered by: IBM via edX

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

Price: $445.50

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:

It’s a very comprehensive course; planned to get ready students and furnish them with abilities required to become effective AI experts and start a profession in applied Deep Learning.

Course #5: Machine Learning

In this online machine learning class, you will find out about the best AI systems. And increase work on actualizing them and getting them to work for yourself. Also, you’ll find out about the hypothetical underpinnings of adapting. Yet additionally gain common skills used to apply these methods to new issues. And finally, you’ll find out about some of Silicon Valley’s accepted procedures in development in accordance with AI and AI.

This course gives a wide prologue to AI, data mining, and factual example acknowledgment. Themes include:

(I) Supervised learning (parametric/non-parametric calculations, bolster vector machines, parts, neural systems).

(ii) But also unsupervised picking up (grouping, dimensionality decrease, recommender frameworks, profound learning).

(iii) And best approaches in AI (inclination/difference hypothesis; development process in AI and AI).

Thus the course will likewise draw from various contextual analyses and applications, with the goal that you’ll additionally figure out how to apply learning calculations to building keen robots, PC vision, medicinal informatics, sound and different regions.

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:

The course will give you the insights to understand data-driven mathematical functions to write softwares that can behave or change its behavior, based on stimulus (data). Andrew Ng is excellent in his teaching methods. So, be prepared to work hard with linear algebra and make efforts to compute things in Matlab/Octave.

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

In the event that you are a product designer who needs to assemble versatile AI-controlled calculations, you have to see how to utilize the devices to fabricate them. This course is a piece of the up and coming Machine Learning in Tensorflow Specialization and will instruct you in best practices for utilizing TensorFlow, a well-known open-source structure for AI.

The Machine Learning course and Deep Learning Specialization from Andrew Ng educate the most significant and primary standards of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization shows you how to utilize TensorFlow to actualize those standards with the goal that you can begin assembling and applying adaptable models to certifiable issues.

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:

It’s a good intro course, but Google Colab assignments need to be improved. And it’s a great course to get started with building Convolutional Neural Networks in Keras for building Image Classifiers. This is probably the best way to get beginners into Deep Learning for Computer Vision.

Course #7: Mathematics for Machine Learning: Linear Algebra

This seminar on Linear Algebra takes a glance at what direct polynomial math is and how it identifies with vectors and networks. And it looks at what vectors and grids are and how to function with them, including the knotty issue of eigenvalues and eigenvectors. And how to utilize these to take care of issues. Finally, it sees how to utilize these to accomplish fun things with datasets – like how to pivot pictures of countenances and how to remove eigenvectors to take a look at how the Pagerank calculation functions.

Furthermore, you will be executing a portion of these thoughts in code, not simply on pencil and paper. Towards the finish of the course, you’ll compose code squares and experience Jupyter note pads in Python. But don’t worry. Because these will be very short, focused on the ideas. And will guide you through carefully in case you have never coded in your life before.

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:

It’s an amazing course with great instructors. And the sheer amount of working linear algebra knowledge you get from this single course is substantial. It has already helped solidify my learning in other ML and AI courses.

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Course #8: Structuring Machine Learning Projects

You will figure out how to assemble an effective AI venture from this course. If you want to be an expert in AI, and know how to set direction for your team’s work, this course will show you how. Quite a bit of this substance has never been educated somewhere else. And it is drawn from the instructor’s experience building and transporting numerous profound learning items.

Likewise, this machine learning course has two pilot training programs that let you practice basic leadership as an AI venture pioneer. Thus you will gain industry experience that you may somehow or another get simply following quite a while of ML work understanding. This is the third course in 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. 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:

This is an independent course, and you can take this such a long time as you have fundamental AI information. Going beyond the technical details, this part of the course goes into the high level view on how to direct your efforts in a ML project. Really enjoyable and useful.

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

This 2-week quickened on-request course acquaints members with the Big Data and Machine Learning abilities of Google Cloud Platform (GCP). It gives a brisk diagram of the Google Cloud Platform. But takes a deeper look of the information handling abilities. Toward the finish of this course, members will have the option to identify the reason and estimation of the key Big Data and Machine Learning items in the Google Cloud Platform. Use CloudSQL and Cloud Dataproc to relocate existing MySQL and Hadoop/Pig/Spark/Hive remaining tasks at hand to Google Cloud Platform and more.

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.

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:

Users have found this course to be ‘overall a good curated course’ to help understand the GCP offerings. And high level architecture of how their offerings fit in the current landscape. This is a fundamental course, aimed to improve basics. So, the content is easy to follow along.

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

In this machine learning training, you will get hands-on involvement in AI from a progression of functional contextual analyses. Toward the finish of the primary course you will have examined how to foresee house costs dependent on house-level highlights. And break down notion from client audits, recover records of intrigue, prescribe items, and quest for pictures. Through hands-on training with these utilization cases, you will have the option to apply AI strategies in a wide scope of spaces.

This first course treats the AI strategy as a black box. Utilizing this reflection, you will concentrate on understanding assignments of enthusiasm, coordinating these errands to AI apparatuses, and surveying the nature of the yield. In resulting courses, you will dig into the segments of this black box by looking at models and calculations. So, together these pieces structure the AI pipeline, which you will use in creating canny applications.

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:

‘Great course’ as users labeled it; well designed and delivered by trainers with the help of case study and great examples. The forums and discussions are really useful and helpful while doing assignments.

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:

This online course explains the underlying concepts well and even if you are already familiar with deep neural networks it’s a great complementary course for any pieces you may have missed previously.

Course #12: Deep Learning Specialization

On the off chance that you need to break into AI, this specialization will assist you with doing as such. Profound Learning is one of the most exceptionally looked for after aptitudes in tech. We will assist you with getting the hang of Deep Learning. In five courses, you will gain proficiency with the establishments of Deep Learning, see how to construct neural systems, and figure out how to lead effective AI ventures.

You will find out about Convolutional systems. And you will chip away at contextual investigations from medicinal services, self-ruling driving, communication via gestures perusing, music age, and characteristic language preparing. Get to ace the hypothesis, yet in addition perceive how it is applied in industry. And rehearse every one of these thoughts in Python and in TensorFlow, which we will instruct.

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:

This course will assist you with acing Deep Learning, see how to apply it, and fabricate a vocation in AI. The content in it will give you a better understanding of what a NN does, for instance. After this online specialization Machine learning won’t look like a black box anymore.

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

This course has been structured by two expert Data Scientists with the goal that they can share their insight and assist you with learning complex hypothesis, calculations and coding libraries in a basic way. They will walk you bit by bit into the world of Machine Learning. With each instructional exercise you will grow new aptitudes and improve your comprehension of this difficult yet worthwhile sub-field of Data Science.

In addition, the course is stuffed with functional activities which depend on genuine models. So not exclusively will you become familiar with the hypothesis, yet you will likewise get a few hands-on works on building your own models. Also, as a little something extra, this course incorporates both Python and R code layouts which you can download and use individually extends.

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:

It’s a highly recommended course if you want to get some hands on experience of Implementing Machine Learning algorithms with proper understanding. The intuition videos provide a good framework to understand the coding tutorials that follow each concept. Overall the course is very well structured covering a large portion of ML universe.

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

In this course you will have an opportunity to both work with and understand when Tensorflow is better and when PyTorch is the way to go. So, throughout the tutorials, the two are compared. And you will keep on receiving tips and ideas on which could work best in certain circumstances. Scikit – learn the most advanced Machine Learning library. It will mainly be used to evaluate the performance of models with the most relevant technique, k-Fold Cross Validation; to improve models with effective Parameter Tuning and to preprocess our data, so that models can learn in the best conditions.

This whole course is based on Python and in every single section you will be getting hours and hours of invaluable hands-on practical coding experience. Throughout the course we will be using Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most efficiently.

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:

For those looking for a beginner to intermediate level of knowledge in Deep Learning, this course is definitely recommended. Because the concepts are explained very clearly and in simple language. To conclude, this course will also help gain advanced level knowledge as the basic concepts become clear.

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

In this course, students will figure out how to gauge the probability distribution of a sequence of random variables. Gradient descent can be used to optimize any objective function. Thus this course shows students how they can use gradient descent to solve for the optimal parameters of an HMM, as an alternative to the popular expectation-maximization algorithm. And it will be done in Theano and Tensorflow, which are well known libraries for profound learning. Thus this is going to show you how to function with arrangements in Theano and Tensorflow.

Furthermore, this course is going to experience the numerous pragmatic utilizations of Markov models and concealed Markov models. It focuses on how Markov models can be utilized to investigate how individuals cooperate with your site. And fix issue zones like high bounce rate, which could be influencing your SEO. Also, we’ll manufacture language models that can be utilized to distinguish an essayist and even produce content. And we’ll see what is perhaps the latest and productive use of Markov models – Google’s Page Rank calculation.

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:

The instructor is incredibly knowledgeable. And able to explain the theory concisely. A very in-depth course regarding math, too. Thus users claimed it as ‘one of the best courses around’ for those serious about machine learning.

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

This course talks about the K-Nearest Neighbor calculation. It’s amazingly basic and instinctive. And it’s an extraordinary first arrangement calculation to learn. It focuses on certain manners by which KNN can come up short. Furthermore, it’s imperative to know both the focal points and inconveniences of every calculation considered. Additionally, Naive Bayes Classifier and the General Bayes Classifier are also discussed here.

Similarly, students will perceive how they can change the Bayes Classifier into a direct and quadratic classifier to accelerate their computations. Decision Tree calculation and Perceptron calculation are also discussed here. Perceptrons are the precursor of neural systems and profound adapting. So, they are imperative to examine with regards to AI. Furthermore, the advantages and disadvantages of each approach are looked upon in detail. Thus this class tops things off with a genuine model by composing a web administration that runs an AI model and makes forecasts. And that is something that genuine organizations do and profit from.

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:

A great course that builds up on your knowledge of AI. But it has the following prerequisites: calculus (for some parts); probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule); Python coding: if/else, loops, lists, dicts, sets; Numpy, Scipy, Matplotlib etc. To conclude, every concepts in this course is well-explained such that you won’t ever forget it.

Course #17: Machine Learning with Javascript

There are numerous online trainings on Machine Learning effectively accessible. This course has been made to be the best prologue to the theme. No subject is left untouched. And this course incorporates Javascript. Because machine learning with Javascript is simpler to learn than with Python. In spite of the fact that it is gigantically prominent, Python is an ‘expressive’ language, which is a code-word that signifies ‘a confounding language’. Most of ML courses online move around the confounding themes of math. And that is the objective of this course – for students to comprehend the precise math and programming procedures utilized in the most widely recognized ML calculations.

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:

If you want to learn about machine learning then this is the course to go. It uses tensorflow.js. But that doesn’t really matter. Because the theory is the same and with this explanation it will click in your head.

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

Data Science has been positioned the main employment on Glassdoor. Consequently, the normal pay of an information researcher is over $120,000 in the United States as indicated by stats. Information Science is a remunerating vocation that enables you to settle a portion of the world’s most fascinating issues. Thus this online course is intended for both complete beginners with no programming experience. But also for experienced designers hoping to make the jump to Data Science.

So, this extensive course is similar to other Data Science bootcamps normally costing a huge number of dollars. However, you can gain proficiency with such data at a small amount of the expense! And with more than 100 HD video talks and nitty gritty code scratch pad for each lecture, this is one of the most extensive course for information science and AI on Udemy. Furthermore, it shows you how to program with R, how to make astonishing information representations. And how to utilize Machine Learning with R.

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:

This course is great. Because it puts you in more challenging situations which are, from the other side, feasible. And it gives you a great sense of learning. Furthermore, it has many useful materials and foundations, good for systematizing basic concepts. Also, very good exercises, challenging but well-adjusted so students don’t lose self-confidence. Thus it gives you a good basis for further learning and very good literature to master machine learning.

Course #19: Deep Learning: Advanced Computer Vision

In this course, you’ll perceive how we can transform a CNN into an article recognition framework, that orders pictures as well as can find each object in a picture and anticipate its name. You can envision that such an assignment is a fundamental essential for self-driving vehicles. Thus it must have the option to recognize vehicles, people on foot, bikes, traffic lights, and so forth progressively.

So, this course looks at a cutting edge calculation called SSD which is both quicker and more exact than its antecedents. Another very prominent PC vision task that utilizes CNNs is called neural style transfer. This is the place you take one picture called the content image. And another called style image; you consolidate these to make a completely new picture. Furthermore, one of the significant topics of this course is that it’s moving endlessly from the CNN itself, to frameworks including CNNs. Finally, this content is based on no convoluted low-level code, for example, that written in Tensorflow, Theano, or PyTorch.

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:

This is a very detailed course about computer vision. Especially one you will find yourself coming back to to get more details. It is a g great balance of theory with practice. However, it will require you to go through it at least twice before you grasp everything.

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

Linear regression is the least complex AI model you can adapt. Yet you’ll be coming back to it for quite a long time to come. Hence this is a good introductory course in case you’re keen on making your first strides in the fields of profound learning; AI and information science. The principal segment tells you the best way to utilize 1-D direct relapse to demonstrate that Moore’s Law is valid. Learn about stretching 1-D direct relapse to any-dimensional straight relapse – at the end of the day, how to make an AI model that can gain from different information sources. Also apply multi-dimensional straight relapse to foreseeing a patient’s systolic circulatory strain given their age and weight.

Furthermore, this course doesn’t require any outer materials. Everything required (Python, and some Python libraries) can be acquired for free. So if you’re a developer and need to upgrade your coding capacities by finding out about information science, this course is for you. But If you have a specialized foundation, and need to realize how to apply your abilities as a product architect or “programmer”, this course might be valuable even then.B Because it centers around how to construct and comprehend, not rather than just utilize.

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 to the point. Students got a reasonable aptitude for math and it’s not difficult to follow along. The best thing about the course is the balance between theory and implementation: The math is not dumbed down or left out. You get a lot of implementation and coding practices from this machine learning certification.

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