introduction to deep learning coursera assignment answers

Deep-Learning-Specialization-Coursera

This repo contains the updated version of all the assignments/labs (done by me) of deep learning specialization on coursera by andrew ng. it includes building various deep learning models from scratch and implementing them for object detection, facial recognition, autonomous driving, neural machine translation, trigger word detection, etc., deep learning specialization coursera [updated version 2021].

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[!IMPORTANT] Check our latest paper (accepted in ICDAR’23) on Urdu OCR

This repo contains all of the solved assignments of Coursera’s most famous Deep Learning Specialization of 5 courses offered by deeplearning.ai

Instructor: Prof. Andrew Ng

This Specialization was updated in April 2021 to include developments in deep learning and programming frameworks. One of the most major changes was shifting from Tensorflow 1 to Tensorflow 2. Also, new materials were added. However, Most of the old online repositories still don’t have old codes. This repo contains updated versions of the assignments. Happy Learning :)

Programming Assignments

Course 1: Neural Networks and Deep Learning

• W2A1 - Logistic Regression with a Neural Network mindset
• W2A2 - Python Basics with Numpy
• W3A1 - Planar data classification with one hidden layer
• W3A1 - Building your Deep Neural Network: Step by Step¶
• W3A2 - Deep Neural Network for Image Classification: Application

Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

• W1A1 - Initialization
• W1A2 - Regularization
• W1A3 - Gradient Checking
• W2A1 - Optimization Methods
• W3A1 - Introduction to TensorFlow

Course 3: Structuring Machine Learning Projects

• There were no programming assignments in this course. It was completely thoeretical.
• Here is a link to the course

Course 4: Convolutional Neural Networks

• W1A1 - Convolutional Model: step by step
• W1A2 - Convolutional Model: application
• W2A1 - Residual Networks
• W2A2 - Transfer Learning with MobileNet
• W3A1 - Autonomous Driving - Car Detection
• W3A2 - Image Segmentation - U-net
• W4A1 - Face Recognition
• W4A2 - Neural Style transfer

Course 5: Sequence Models

• W1A1 - Building a Recurrent Neural Network - Step by Step
• W1A2 - Character level language model - Dinosaurus land
• W1A3 - Improvise A Jazz Solo with an LSTM Network
• W2A1 - Operations on word vectors
• W2A2 - Emojify
• W3A1 - Neural Machine Translation With Attention
• W3A2 - Trigger Word Detection
• W4A1 - Transformer Network
• W4A2 - Named Entity Recognition - Transformer Application
• W4A3 - Extractive Question Answering - Transformer Application

I’ve uploaded these solutions here, only for being used as a help by those who get stuck somewhere. It may help them to save some time. I strongly recommend everyone to not directly copy any part of the code (from here or anywhere else) while doing the assignments of this specialization. The assignments are fairly easy and one learns a great deal of things upon doing these. Thanks to the deeplearning.ai team for giving this treasure to us.

Connect with me

Name: Abdur Rahman

Institution: Indian Institute of Technology Delhi

Find me on:

Neural Networks and Deep Learning Coursera Quiz Answers

• In Data Science Quiz
• In Deep Learning Specialization
• On February 23, 2024

Get All Weeks Neural Networks and Deep Learning Coursera Quiz Answers

Table of contents, neural networks and deep learning week 01 quiz answers.

Q1. What does the analogy “AI is the new electricity” refer to?

Q2. Which of these are reasons for Deep Learning recently taking off? (Check the three options that apply.)

1. We have access to a lot more computational power. 2. We have access to a lot more data 3. Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.

Q3. Recall this diagram of iterating over different ML ideas. Which of the statements below are true? (Check all that apply.)

Being able to try out ideas quickly allows deep learning engineers to iterate more quickly.

Recent progress in deep learning algorithms has allowed us to train good models faster (even without changing the CPU/GPU hardware).

Faster computation can help speed up how long a team takes to iterate to a good idea.

Q4. When an experienced deep learning engineer works on a new problem, they can usually use insight from previous problems to train a good model on the first try, without needing to iterate multiple times through different models. True/False?

Q5. Which one of these plots represents a ReLU activation function?

Q6. Images for cat recognition is an example of “structured” data because it is represented as a structured array in a computer. True/False?

Q7. A demographic dataset with statistics on different cities’ populations, GDP per capita, and economic growth is an example of “unstructured” data because it contains data coming from different sources. True/False?

Q8. Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? (Check all that apply.)

Q9. In this diagram which we hand-drew in lecture, what do the horizontal axis (x-axis) and vertical axis (y-axis) represent?

y-axis (vertical axis) is the performance of the algorithm.

Q10. Assuming the trends described in the previous question’s figure are accurate (and I hope you got the axis labels right), which of the following are true? (Check all that apply.)

Increasing the training set size generally does not hurt an algorithm’s performance, and it may help significantly.

Neural Networks and Deep Learning Week 02 Quiz Answers

Q1. What does a neuron compute?

Q3. Suppose img is a (32,32,3) array, representing a 32×32 image with 3 color channels red, green and blue. How do you reshape this into a column vector?

Q4. Consider the two following random arrays aa and bb:

a = np. random.randn(2, 3)a=np.random.randn(2,3) # a.shape = (2, 3)a.shape=(2,3)

b = np. random.randn(2, 1)b=np.random.randn(2,1) # b.shape = (2, 1)b.shape=(2,1)

c = a + b c=a+b

What will be the shape of cc?

Q5. Consider the two following random arrays aa and bb:

a = np. random.randn(4, 3)a=np.random.randn(4,3) # a.shape = (4, 3)a.shape=(4,3)

b = np. random.randn(3, 2)b=np.random.randn(3,2) # b.shape = (3, 2)b.shape=(3,2)

c = a*bc=a∗b

What will be the shape of the cc?

Q6. Recall that X = [x^{(1)} x^{(2)} … x^{(m)}] X =[ x (1) x (2)… x ( m )]. What is the dimension of X?

Q7. Recall that np. dot(a,b)np.dot(a,b) performs a matrix multiplication on aa and bb, whereas a*ba∗b performs an element-wise multiplication.

Consider the two following random arrays aa and bb:

a = np. random.randn(12288, 150)a=np.random.randn(12288,150) # a.shape = (12288, 150)a.shape=(12288,150)

b = np. random.randn(150, 45)b=np.random.randn(150,45) # b.shape = (150, 45)$$

c = np.dot(a,b)c=np.dot(a,b)

What is the shape of cc?

c.shape = (12288, 150)

Q8. Consider the following code snippet:

# a.shape = (3,4)a.shape=(3,4)

b.shape = (4,1)b.shape=(4,1)

for i in range(3): for j in range(4): c[i][j] = a[i][j] + b[j]c[i][j]=a[i][j]+b[j]

How do you vectorize this?

Q9. Consider the following code:

a = np. random.randn(3, 3)a=np.random.randn(3,3)

b = np. random.randn(3, 1)b=np.random.randn(3,1)

What will be cc? (If you’re not sure, feel free to run this in Python to find out).

• This will multiply a 3×3 matrix a with a 3×1 vector, thus resulting in a 3×1 vector. That is, c.shape = (3,1).
• This will invoke broadcasting, so b is copied three times to become (3,3), and *∗ is an element-wise product so c.shape will be (3, 3)
• This will invoke broadcasting, so b is copied three times to become (3, 3), and *∗ invokes a matrix multiplication operation of two 3×3 matrices so c.shape will be (3, 3)
• It will lead to an error since you cannot use “*” to operate on these two matrices. You need to instead use np. dot(a,b)

Q10. Consider the following computation graph.

What is the output J?

Neural Networks and Deep Learning Week 03 Quiz Answers

Q1. Which of the following are true? (Check all that apply.)

Q2. The tanh activation is not always better than the sigmoid activation function for hidden units because the mean of its output is closer to zero, and so it centers the data, making learning complex for the next layer. True/False?

Q3. Which of these is a correct vectorized implementation of forward propagation for layer l , where 1≤ l ≤ L ?

Q4. You are building a binary classifier for recognizing cucumbers (y=1) vs. watermelons (y=0). Which one of these activation functions would you recommend using for the output layer?

Q5. Consider the following code:

A = np. random.randn(4,3)B =

B = np.sum(A, axis = 1, keepdims = True)

What will be B.shape? (If you’re not sure, feel free to run this in Python to find out).

Q6. Suppose you have built a neural network. You decide to initialize the weights and biases to be zero. Which of the following statements is true?

Q7. Logistic regression’s weights w should be initialized randomly rather than to all zeros, because if you initialize to all zeros, then logistic regression will fail to learn a useful decision boundary because it will fail to “break symmetry”, True/False?

Q8. You have built a network using the tanh activation for all the hidden units. You initialize the weights to relatively large values, using np.random.randn(..,..)*1000. What will happen?

Q9. Consider the following 1 hidden layer neural network:

Which of the following statements is True? (Check all that apply).

Q10. In the same network as the previous question, what are the dimensions of Z [1] and A^{[1]} A [1]?

Neural Networks and Deep Learning Week 04 Quiz Answers

Q1. What is the “cache” used for in our implementation of forward propagation and backward propagation?

• It is used to cache the intermediate values of the cost function during training.

Q2. Among the following, which ones are “hyperparameters”? (Check all that apply.)

Q3. Which of the following statements is true?

Q4. Vectorization allows you to compute forward propagation in an LL-layer neural network without an explicit for-loop (or any other explicit iterative loop) over the layers l=1, 2, …,L. True/False?

Q5. Assume we store the values for n^{[l]} in an array called layer_dims, as follows: layer_dims = [n_xn x, 4,3,2,1]. So layer 1 has four hidden units, layer 2 has 3 hidden units, and so on. Which of the following for-loops will allow you to initialize the parameters for the model?

Q6. Consider the following neural network.

Q7. During forward propagation, in the forward function for a layer ll you need to know what is the activation function in a layer (Sigmoid, tanh, ReLU, etc.). During backpropagation, the corresponding backward function also needs to know what is the activation function for layer ll, since the gradient depends on it. True/False?

Q8. There are certain functions with the following properties:

(i) To compute the function using a shallow network circuit, you will need a large network (where we measure size by the number of logic gates in the network), but (ii) To compute it using a deep network circuit, you need only an exponentially smaller network. True/False?

Q9. Consider the following 2 hidden-layer neural networks:

Q10. Whereas the previous question used a specific network, in the general case what is the dimension of W^{[l]}, the weight matrix associated with layer ll?

Conclusion:

In conclusion, the Neural Networks and Deep Learning Coursera Quiz Answers provide a comprehensive understanding of key concepts and principles in the field of neural networks and deep learning. These answers not only serve as a valuable resource for learners seeking to solidify their knowledge but also offer insights into solving practical problems using deep learning techniques.

Get all Course Quiz Answers of Deep Learning Specialization

Course 01: Neural Networks and Deep Learning Coursera Quiz Answers

Course 02: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Quiz Answers

Course 03: Structuring Machine Learning Projects Coursera Quiz Answers

Course 04: Convolutional Neural Networks Coursera Quiz Answers

Course 05: Sequence Models Coursera Q

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Coursera: Neural Networks and Deep Learning (Week 4A) [Assignment Solution] - deeplearning.ai

▸  building your deep neural network: step by step. i have recently completed the neural networks and deep learning course from coursera by deeplearning.ai while doing the course we have to go through various quiz and assignments in python. here, i am sharing my solutions for the weekly assignments throughout the course. these solutions are for reference only. >  it is recommended that you should solve the assignments by yourself honestly then only it makes sense to complete the course. >  but, in case you stuck in between, feel free to refer to the solutions provided by me., don't just copy paste the code for the sake of completion.  even if you copy the code, make sure you understand the code first. click here : coursera: neural networks & deep learning (week 3) click here:  coursera: neural networks & deep learning (week 4b) scroll down  for  coursera: neural networks & deep learning (week 4a) assignments . (adsbygoogle = window.adsbygoogle || []).push({}); recommended machine learning courses: coursera: machine learning    coursera: deep learning specialization coursera: machine learning with python coursera: advanced machine learning specialization udemy: machine learning linkedin: machine learning eduonix: machine learning edx: machine learning fast.ai: introduction to machine learning for coders, building your deep neural network: step by step, in this notebook, you will implement all the functions required to build a deep neural network. in the next assignment, you will use these functions to build a deep neural network for image classification. after this assignment you will be able to: use non-linear units like relu to improve your model build a deeper neural network (with more than 1 hidden layer) implement an easy-to-use neural network class notation : superscript  [ l ]  denotes a quantity associated with the  l t h  layer. example:  a [ l ]  is the  l t h  layer activation.  w [ l ]  and  b [ l ]  are the  l t h  layer parameters. superscript  ( i )  denotes a quantity associated with the  i t h  example. example:  x ( i )  is the  i t h  training example. lowerscript  i i  denotes the  i t h  entry of a vector. example:  a [ l ] i  denotes the  i t h  entry of the  l t h  layer's activations). let's get started, 1 - packages, let's first import all the packages that you will need during this assignment. numpy  is the main package for scientific computing with python. matplotlib  is a library to plot graphs in python. dnn_utils provides some necessary functions for this notebook. testcases provides some test cases to assess the correctness of your functions np.random.seed(1) is used to keep all the random function calls consistent. it will help us grade your work. please don't change the seed., 2 - outline of the assignment.

• Initialize the parameters for a two-layer network and for an  L -layer neural network.
• Complete the LINEAR part of a layer's forward propagation step (resulting in  Z [ l ] ).
• We give you the ACTIVATION function (relu/sigmoid).
• Combine the previous two steps into a new [LINEAR->ACTIVATION] forward function.
• Stack the [LINEAR->RELU] forward function L-1 time (for layers 1 through L-1) and add a [LINEAR->SIGMOID] at the end (for the final layer  L ). This gives you a new L_model_forward function.
• Compute the loss.
• Complete the LINEAR part of a layer's backward propagation step.
• We give you the gradient of the ACTIVATE function (relu_backward/sigmoid_backward)
• Combine the previous two steps into a new [LINEAR->ACTIVATION] backward function.
• Stack [LINEAR->RELU] backward L-1 times and add [LINEAR->SIGMOID] backward in a new L_model_backward function
• Finally update the parameters.

Check-out our free tutorials on IOT (Internet of Things):

3 - Initialization

3.1 - 2-layer neural network.

• The model's structure is:  LINEAR -> RELU -> LINEAR -> SIGMOID .
• Use random initialization for the weight matrices. Use  np.random.randn(shape)*0.01  with the correct shape.
• Use zero initialization for the biases. Use  np.zeros(shape) .

3.2 - L-layer Neural Network

• The model's structure is  [LINEAR -> RELU]  × ×  (L-1) -> LINEAR -> SIGMOID . I.e., it has  L − 1 L − 1  layers using a ReLU activation function followed by an output layer with a sigmoid activation function.
• Use random initialization for the weight matrices. Use  np.random.randn(shape) * 0.01 .
• Use zeros initialization for the biases. Use  np.zeros(shape) .
• We will store  n [ l ] n [ l ] , the number of units in different layers, in a variable  layer_dims . For example, the  layer_dims  for the "Planar Data classification model" from last week would have been [2,4,1]: There were two inputs, one hidden layer with 4 hidden units, and an output layer with 1 output unit. Thus means  W1 's shape was (4,2),  b1  was (4,1),  W2  was (1,4) and  b2  was (1,1). Now you will generalize this to  L L  layers!
• Here is the implementation for  L = 1 L = 1  (one layer neural network). It should inspire you to implement the general case (L-layer neural network).

4 - Forward propagation module

4.1 - linear forward.

• LINEAR -> ACTIVATION where ACTIVATION will be either ReLU or Sigmoid.
• [LINEAR -> RELU]  × ×  (L-1) -> LINEAR -> SIGMOID (whole model)

4.2 - Linear-Activation Forward

• Sigmoid :  σ ( Z ) = σ ( W A + b ) = 1 1 + e − ( W A + b ) . We have provided you with the  sigmoid  function. This function returns  two  items: the activation value " a " and a " cache " that contains " Z " (it's what we will feed in to the corresponding backward function). To use it you could just call: A , activation_cache = sigmoid ( Z )
• ReLU : The mathematical formula for ReLu is  A = R E L U ( Z ) = m a x ( 0 , Z ) A = R E L U ( Z ) = m a x ( 0 , Z ) . We have provided you with the  relu  function. This function returns  two items: the activation value " A " and a " cache " that contains " Z " (it's what we will feed in to the corresponding backward function). To use it you could just call: A , activation_cache = relu ( Z )

d) L-Layer Model

• Use the functions you had previously written
• Use a for loop to replicate [LINEAR->RELU] (L-1) times
• Don't forget to keep track of the caches in the "caches" list. To add a new value  c  to a  list , you can use  list.append(c) .

5 - Cost function

6 - backward propagation module.

• LINEAR backward
• LINEAR -> ACTIVATION backward where ACTIVATION computes the derivative of either the ReLU or sigmoid activation
• [LINEAR -> RELU]  × ×  (L-1) -> LINEAR -> SIGMOID backward (whole model)

6.1 - Linear backward

6.2 - Linear-Activation backward

• sigmoid_backward : Implements the backward propagation for SIGMOID unit. You can call it as follows:
• relu_backward : Implements the backward propagation for RELU unit. You can call it as follows:

6.3 - L-Model Backward

6.4 - Update Parameters

7 - conclusion.

• A two-layer neural network
• An L-layer neural network

hi bro...i was working on the week 4 assignment .i am getting an assertion error on cost_compute function.help me with this..but the same function is working for the l layer model AssertionError Traceback (most recent call last) in () ----> 1 parameters = two_layer_model(train_x, train_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost= True) in two_layer_model(X, Y, layers_dims, learning_rate, num_iterations, print_cost) 46 # Compute cost 47 ### START CODE HERE ### (≈ 1 line of code) ---> 48 cost = compute_cost(A2, Y) 49 ### END CODE HERE ### 50 /home/jovyan/work/Week 4/Deep Neural Network Application: Image Classification/dnn_app_utils_v3.py in compute_cost(AL, Y) 265 266 cost = np.squeeze(cost) # To make sure your cost's shape is what we expect (e.g. this turns [[17]] into 17). --> 267 assert(cost.shape == ()) 268 269 return cost AssertionError:

Hey,I am facing problem in linear activation forward function of week 4 assignment Building Deep Neural Network. I think I have implemented it correctly and the output matches with the expected one. I also cross check it with your solution and both were same. But the grader marks it, and all the functions in which this function is called as incorrect. I am unable to find any error in its coding as it was straightforward in which I used built in functions of SIGMOID and RELU. Please guide.

hi bro iam always getting the grading error although iam getting the crrt o/p for all

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Course 1: Neural Networks and Deep Learning Coursera Quiz Answers – Assignment Solutions

Course 2: improving deep neural networks: hyperparameter tuning, regularization and optimization coursera quiz answers – assignment solutions, course 3: structuring machine learning projects coursera quiz answers – assignment solutions, course 4: convolutional neural networks coursera quiz answers – assignment solutions, week 3 assignment – updated, course 5: sequence models coursera quiz answers – assignment solutions.

Neural Networks and Deep Learning Coursera Quiz Answers Neural Networks and Deep Learning Coursera Assignment Solutions Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Coursera Quiz Answers Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Coursera Assignment Solutions Structuring Machine Learning Projects Coursera Quiz Answers Structuring Machine Learning Projects Coursera Assignment Solutions Convolutional Neural Networks Coursera Quiz Answers Convolutional Neural Networks Coursera Assignment Solutions Sequence Models Coursera Quiz Answers Sequence Models Coursera Assignment Solutions

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1 - 25 of 230 Reviews for Hands-on Introduction to Linux Commands and Shell Scripting

May 18, 2022

There is so much information missing in the labs in this course esp starting from Week 3. The videos it seems like were put together in a hurry with crucial information missing and labs are even worse than the videos. They are expecting one to find, set, unset files in extremely long scripts without providing any information on how it is done. Furthermore, there is little to no information given on why certain commands are called what they are called making this course not at all intuitive. I wish someone else has reviewed this course before putting it online. Such a wasted opportunity to educate people.

Jan 17, 2022

This need a lot more hands-on experience and explaining the *why* of commands. The course never explained any logic behind operators and commands, so it was never clear how everything worked.

By Amanda W

Aug 2, 2022

This course is terrible for beginners!! None of the modules go in depth, it was difficult to understand because none of the commands are explained, and the final project is super advanced for what is actually taught in the class. I feel like I learned almost nothing!

By Mohd J H

Nov 13, 2022

would prefer a live instructor. not a generic AI voice reading a PPT. would not recommend.

Mar 25, 2023

The course was not uptown the mark.

The whole course was explained by a google teacher?

This is actually not acceptable from IBM

Feb 28, 2022

poor content

By Mohamad G

Jul 25, 2023

This course is so bad, the instructions are insufficient, and the teachings compared to the project are different, highly do not recommend

By Deleted A

Jan 3, 2023

it's really not easy and so clear for any beginner to start with!

By Mateusz K

Jan 24, 2024

Everything that follows, is just my own personal opinion, if not stated otherwise. It is based on my observations and interactions with teaching staff in the course discussion forums (for the last few months). This is a very poorly executed course. As of today (23 Jan 2024) this course for me still does not meet the criteria of a "good resource for learning" the basics of Linux CLI and shell scripting (despite few minor improvements already applied). Some of the topics are hastily skimmed through in the videos, and almost no mechanism presented in the course is thoroughly explained. Many of the "basic concepts" for Bash (and other modern shells) like e.g. function definitions, proper error handling, etc., are not even included in the course. The importance of using correct quotations for variable/parameter expansions is also never mentioned nor explained, although it should be considered essential "best practice". Peer-graded Final Assignment instructions/"guide" contains some errors and not fully tested solutions, advertised as "working correctly". The similar can be said about grading rubrics for the final assignment - which additionally are not focused on the actual results of the code execution, but instead they're focused on the code "looks", as in its visual resemblance (being as close to "identical" as possible) to what the course's staff have written in the grading rubrics. In some cases, if the teaching staff finds a question in a discussion forum a bit more "inconvenient" for them to answer (or they're not fully capable of providing the correct answer to it in a timely manner?), then they would rather: a) ignore the question completely (the least they can do is to not get involved at all) or reply with something similar to: "we'll investigate the issue and get back to you" (the latter "get back to you" almost never happens for a "more complicated" question); b) reply with some irrelevant, impractical, incorrect, or otherwise unuseful information; c) politely ask, to not bring the subject up ever again, as it may "overwhelm the learners"; d) delete the post (most often containing a valid, correct answer to the question) from discussion forums (I see censorship as "means of hiding incompetence"?) e) repeat other learner's correct answer (sometimes only slightly modifying it); f) use and completely rely on AI LLM (e.g. ChatGPT, Bard, or other) and answers provided by it (which are mostly ridiculous), without even basic output verification; g) and also (occasionally) delete any relevant corrections (made by more knowledgeable learners) to previous staff's posts/replies; than admit to any mistakes in the course contents, admit to lack of expertise in the subject matter, and address the questions properly - e.g. by allowing open discussion on potential improvements to the course materials, and implementing all said improvements as soon as possible (after reviewing). These practices have been reported, and as I was assured by the Coursera Support Team, this kind of inappropriate teaching staff's behaviors will be escalated further, so I'm awaiting next developments of the situation. But I still think this kind of inappropriate behaviors are a clear example, of what gives (or rather should give) the Skill-Up Technologies Pvt. Ltd. / SkillUp Online (employers for the most of "teaching staff") a bad reputation. This also might give Coursera courses and IBM Skills Network a really bad publicity in a long-run. Teaching and following "best practices", and answering doubts with relevant, thorough, technical knowledge in the discussion forums of this course... is something you unlikely/rarely find coming from the teaching staff. Generally, teaching staff don't seem to be fit for the job of thoroughly explaining the course materials and teaching the proper "understanding" of your mistakes (as hard as they try to be seen "cooperative"), but as I see it, they're better suited for a) only requesting you to "copy and paste" their code samples, that they believe should work best as a "correct solution" in their opinions (side note, again, "my opinion" - their code doesn't work all that great, and is not written with all the "best practices" in mind) and b) deleting "inconvenient" posts from the discussion forums whenever they please. To sum it up: in its current form, and with its current teaching staff replying in the discussion forums, I'd definitely not recommend this course to anyone, especially beginners. And I'd discourage seeking any "great" learning experience from Skill-Up Technologies Pvt. Ltd. / SkillUp Online - thus I'd strongly suggest searching for some other, better resources on the subjects regarding basics of working with Linux CLI and shell scripting.

Oct 23, 2021

A great start for beginners who wish to nail down the basics of shell scripting

Dec 27, 2021

Good overview, but should have included more using BASH scripts. Conditional statements and loops were not covered. Functions were not covered. Could have included catching errors with "$?". Running a database command in a BASH script to import or export data would be useful. Still a decent course though.

By Hannes B

Feb 14, 2022

I missed more input on actuall scripting. There were no references to loops, if and function definition. all things that you need when writing more advanced scripts

Dec 13, 2022

Some help but lacks pedagogical skill in its design.

By Murilo S

Jan 13, 2024

I have completed several courses as part of the IBM Data Engineering Professional Certificate, and most of them have been exceptional in terms of content and coherence. However, I must express my disappointment with a particular course in the series. The course content seemed incomplete, lacking the depth and breadth that I have come to expect from IBM courses. This made it difficult to gain a comprehensive understanding of the subject matter.

Feb 20, 2022

If you've never used Linux before this may be helpful. But it you have experience with it this course is pretty worthless. I finished it in less than an hour.

By Pedram A

Apr 6, 2022

the material was not sufficient

the last assignment was not clear and the answer of some task was given in the question!!!.

Jun 13, 2023

I had to take the final exam twice because of a bug at coursera, even after i finished the whole course sucessfully!

By BENDIB H

Feb 10, 2022

Very interesting section, I find it is very efficient , sufficient and collects every thing we need with simplified and clear way. thanks to instructor's stuff

By Aniket Y

Dec 18, 2021

It was wonderful experience learning shell scripting. Scheduling jobs was the best part. Thank you instructors

Oct 7, 2021

Mini course indeed but full of new information and great explanation. Thank you.

Nov 7, 2021

really brought together the bash information well. so glad I took this

By Volkert d V

Jan 18, 2024

I have a few qualms on this still good, dense course: (1) - there is a lot of jargon that is taken for granted. For someone new to programming - you can easily get lost - especially when certain programs are only mentioned once and then taken to be known (e.g. Kubernetes, RedHat etc.) A cheat sheet would be very welcome. (2) the videos, while clear, are very dense, and the voice is very mono-tone which became quite a turn-down. To make sure students capture it it would be helpful to have more cross-checking questions during the lecture. (3) the final peer reviewed exercise is tricky and the peer review system doesn't work that great. with all the talk on automated checking in IBMs DevOps course I would think an automated check would be more effective - and could actually point out to you what you did wrong. for example - I had trouble figuring out a certain line of code and I actually asked chatGPT - turns out I had one space too much. in another case I came up with a slightly different code which did the same job. My peer reviewer rated it wrong.

Mar 21, 2023

Great course, thorough introduction and easy to follow.

However, this course and others in the IBM professional certificate are plagued with peers who blatantly mark assessments incorrectly. The fact that you have to rely on peer grading to pass and are subject to their emotional swings is outrageous. This course loses all credibility by associating itself with an online learning education platform that won't rectify a flawed feature.

By Jonathan V C

Aug 19, 2022

If you don't know anything about Linux and commands this is for you, but if you are looking for a more advanced course stay away. I remove 1 star because is too basic and short, and could cover more things, and another star, because peer review is a joke, I got a 90 instead a 100, because the reviewer of my project apparently doesn't understand and take away 2 points from correct answers.

By Muhammad H

Apr 1, 2023

Overall, the course is very nicely designed and implemented except for the last week where they have shell scripting assignment. The way it is collected should be revisited as it kills the user experience by introducing redundant task of manually screenshotting every code snippet separately. Which is hectic in my opinion.