coursera introduction to data science in python assignment solutions

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Professional Certificate Community — karmudesa asked a question.

each time it gives different error.. last error -

Your solution file either prints output to the console (e.g. through the print() function, or it fails to run (e.g. throws an error). You must make sure that the py or ipynb file which you submit does not have errors. The output we received from the ipthon interpretor was: File "/home/marker/grader/assignment3_student_solution.py", line 13 This assignment requires more individual learning then the last one did - you are encouraged to check out the [pandas documentation]( http://pandas.pydata.org/pandas-docs/stable/ ) to find functions or methods you might not have used yet, or ask questions on [Stack Overflow]( http://stackoverflow.com/ ) and tag them as pandas and python related. And of course, the discussion forums are open for interaction with your peers and the course staff. ^ SyntaxError: invalid syntax

• Choosing A Career Path

We can easily resolve the issue by just adding one cell before “Answer One” and in that line import NumPy as np and import pandas as pd and do not import them in any function again. Also do not check the output of any function then your issue will be resolved.

Check if you have any print statements or function calls in the notebook. The grader requires that there be no code except inside the given function declarations and there be no print functions.

If it still shows some error then debug your code because this means your code has errors.

Hope this helps.

meghna_kuhar

I AM GETTING THE SAME PROBLEM

Found the solution to the issue :

• Add a new line before answering the first question where you import numpy and pandas
• Do not import numpy or pandas anywhere else again (you’ve already done it in Step 1)
• Do not check the output of the created function anywhere e.g. answer_five(), answer_two() etc.
• Comment or remove the plot function. It doesn’t affect your score.

check my answer

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Generative AI Foundations

This course is part of Learn Generative AI with LLMs Specialization

Taught in English

Instructor: Edureka

Financial aid available

Recommended experience

Beginner level

A basic understanding of artificial intelligence concepts and familiarity with python programming concepts are beneficial but not mandatory.

What you'll learn

Master Generative AI concepts, apply them in code generation and gain expertise in advanced models like Autoencoders and GANs.

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12 assignments

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There are 4 modules in this course

Welcome to the "Generative AI Foundations" course, a learning journey designed to equip you with a deep understanding of Generative AI, its principles, methodologies, and applications across various domains.

By the end of this course, you will have acquired the knowledge and skills to: - Grasp the foundational concepts and technical intricacies of Generative AI, including its advantages and limitations. - Apply Generative AI for code generation, enhancing your programming efficiency and creativity in Python and other languages. - Master the art of prompt engineering to optimize interactions with AI models like ChatGPT, leading to improved outcomes in code generation and beyond. - Utilize ChatGPT for learning and mastering Python, data science, and software development practices, thereby broadening your technical skill set. - Explore the revolutionary fields of Autoencoders and Generative Adversarial Networks (GANs), understanding their architecture, operation, and applications. - Dive into the world of language models and transformer-based generative models, gaining insights into their mechanisms, applications, and impact on the future of AI. This course is meticulously crafted to cater to a broad audience, including software developers, data scientists, AI enthusiasts, and professionals seeking to leverage Generative AI technologies for innovative solutions. While prior knowledge of Generative AI Fundamentals or Python Coding is helpful, but it is not a prerequisite to complete the course. Whether you're looking to enhance your existing skills or embark on a new career path in the field of AI, this course will provide you with the knowledge, practical skills, and confidence to succeed. Join us on this exciting journey into the world of Generative AI!

Gen AI Foundations

This module is designed to equip learners with a solid understanding of Generative AI principles, models, and applications, setting the stage for more advanced exploration. Through engaging lessons that include videos on the overview of Generative AI, its principles, understanding its models, and the advantages and disadvantages, along with practical applications like code generation and prompt engineering, participants will gain valuable insights. This module also emphasizes ethical considerations and includes practice assignments and discussion prompts to encourage active learning and application of concepts. Whether you're new to AI or looking to enhance your understanding of Generative AI's capabilities, this module provides the essential knowledge base to start your journey.

What's included

16 videos 6 readings 4 assignments 3 discussion prompts

16 videos • Total 85 minutes

• Course Introduction • 4 minutes • Preview module
• Overview of Generative AI • 7 minutes
• Generative AI Principles • 2 minutes
• Generative AI vs. Generative AI Model • 5 minutes
• Understanding Generative AI Models • 6 minutes
• Transformer - Based , Energy - Based & Conditional Generation Models • 3 minutes
• Generative AI for Code Generation • 3 minutes
• Benefits of Generative AI for Code Generation • 4 minutes
• Introduction to ChatGPT • 3 minutes
• Log-in Process • 4 minutes
• Code Generation with ChatGPT • 5 minutes
• Leveraging ChatGPT to Learn Data Science with Python • 6 minutes
• Visualization with Gen AI • 7 minutes
• Exploratory Data Analysis • 4 minutes
• Demonstration on Exploratory Data Analysis • 6 minutes
• Exploratory Data Analysis with the help of ChatGPT • 7 minutes

6 readings • Total 50 minutes

• Course Overview • 5 minutes
• Ethical Considerations in Generative AI: A Guide for Responsible Innovation • 10 minutes
• How to use Discussion Forums • 5 minutes
• ChatGPT Account Creation • 10 minutes
• How Python Professionals can use ChatGPT • 10 minutes
• Generative AI: Foundations, Applications, and Ethical Exploration • 10 minutes

4 assignments • Total 29 minutes

• Knowledge Check: Generative AI-Getting Started • 20 minutes
• Knowledge Check: Generative AI Getting Started • 3 minutes
• Knowledge Check: Prompt Engineering Fundamentals • 3 minutes
• Knowledge Check: Leveraging ChatGPT by Software Developers • 3 minutes

3 discussion prompts • Total 30 minutes

• Balancing Innovation and Ethical Responsibility in Generative AI Applications • 10 minutes
• Optimize Prompt Design • 10 minutes
• Python Development Workflow • 10 minutes

Autoencoders and GANs

This module is crafted to provide an in-depth understanding of how these models function, their architectural nuances, and their wide array of applications in the tech industry. Starting with the basics of Autoencoders, learners will explore the workings and variations of these networks, including Variational Autoencoders (VAEs), and understand their significance in data compression and generative tasks. The journey continues with an exploration of GANs, from their foundational architecture to the nuances of training and the exploration of their diverse variants. Through practical assignments, engaging video content, and focused readings, participants will gain hands-on experience working with these models, culminating in a deeper comprehension of their capabilities and limitations.

10 videos 3 readings 4 assignments 3 discussion prompts

10 videos • Total 57 minutes

• Working of Autoencoders • 10 minutes • Preview module
• Variational Autoencoders • 6 minutes
• Introduction to GAN • 5 minutes
• Working of GAN • 3 minutes
• Basic GAN Architecture • 5 minutes
• Variants of GANs • 6 minutes
• BigGAN • 2 minutes
• Training GANs • 6 minutes
• About GAN • 3 minutes
• Data Compression with Autoencoders • 7 minutes

3 readings • Total 30 minutes

• Variational Autoencoders: Applications and Insights • 10 minutes
• Technical Symphony of Variational Autoencoders in Data Compression • 10 minutes
• Summary and Consolidation of Autoencoders and GANs • 10 minutes
• Knowledge Check: Autoencoders and GANs • 20 minutes
• Knowledge Check: Basic Autoencoders • 3 minutes
• Knowledge Check: GAN Architecture • 3 minutes
• Knowledge Check: GAN Practicals • 3 minutes
• Transforming Industries with Autoencoders and VAEs: Benefits and Challenges • 10 minutes
• Adversarial Dynamics in GANs • 10 minutes
• Overcoming GAN Challenges for Enhanced Creativity and Realism • 10 minutes

Language Models and Transformer-based Generative Models

This module provides an in-depth exploration of Language Models and Transformer-based Generative Models, foundational elements in natural language processing and artificial intelligence. Starting with an overview of language models, it progresses to cover the revolutionary transformer architecture, detailing its attention mechanism and various advanced models. The module then shifts focus to groundbreaking models such as GPT and BERT, examining their development, capabilities, and the wide array of applications they enable in the AI domain. Concluding with comprehensive assessments, including practice and graded assignments on cutting-edge topics like VAEs and GANs, the module offers a holistic understanding of how these technologies drive innovation in AI research and applications.

9 videos 4 readings 3 assignments

9 videos • Total 46 minutes

• Exploring Language Models • 5 minutes • Preview module
• Types of Language Models • 6 minutes
• Transfer Models • 5 minutes
• Applications of Language Models • 7 minutes
• Summarization and Search • 2 minutes
• Introduction to GPT • 4 minutes
• Understanding GPT • 4 minutes
• BERT • 5 minutes
• Inference in BERT • 4 minutes

4 readings • Total 40 minutes

• The Transformer Architecture: Attention Mechanism • 10 minutes
• Advanced Transformer Models • 10 minutes
• Applications of Transformer Based Models • 10 minutes
• Module Summary: Exploring Language Models and Transformer-Based Generative Models • 10 minutes

3 assignments • Total 26 minutes

• Knowledge Check: Language Models and Transformer-based Generative Models • 20 minutes
• Knowledge Check: Language Models • 3 minutes
• Knowledge Check: GPT and BERT • 3 minutes

Course Wrap-up and Assessment

This final module is designed to consolidate the knowledge and skills learners have acquired throughout the course. It starts with a Practice Project, encouraging learners to apply their understanding in a hands-on manner, thus bridging the gap between theoretical knowledge and practical application. Following this, the module offers a Graded Assignment on Gen AI Fundamentals, aimed at rigorously evaluating the learners' grasp of the key concepts, techniques, and applications explored in the course.

1 video 2 readings 1 assignment

1 video • Total 1 minute

• Course Summary • 1 minute • Preview module

2 readings • Total 20 minutes

• Streamlit Documentation • 10 minutes
• Practice Project • 10 minutes

1 assignment • Total 30 minutes

• End Course Knowledge Check • 30 minutes

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Frequently asked questions

What is the generative ai foundations course about.

The Generative AI Foundations course is designed to introduce learners to the fundamentals of generative artificial intelligence. The course covers a wide range of topics, including the principles of generative AI, code generation with ChatGPT, prompt engineering, leveraging ChatGPT for learning Python and software development, autoencoders, GANs (Generative Adversarial Networks), language models, and transformer-based generative models. Through videos, readings, and practical assignments, learners will gain a comprehensive understanding of generative AI technologies and their applications.

Who should enroll in this course?

This course is ideal for anyone interested in understanding and working with generative AI technologies, including software developers, data scientists, researchers, and students in computer science or related fields. Prior knowledge of Python and basic concepts of machine learning will be helpful but not mandatory.

How is the course content delivered?

The course content is delivered through a mix of instructional videos, reading materials, and practice assignments. Each lesson includes videos that cover key topics, readings to deepen your understanding, and practical assignments to apply what you've learned. There are also discussion prompts to encourage interaction among students. The course content is delivered through a mix of instructional videos, reading materials, and practice assignments. Each lesson includes videos that cover key topics, readings to deepen your understanding, and practical assignments to apply what you've learned. There are also discussion prompts to encourage interaction among students.

Are there any graded assignments or assessments?

Yes, the course includes both practice assignments and graded assignments. Practice assignments are designed to reinforce learning and allow students to apply concepts in practical scenarios. Graded assignments are used to assess understanding of the course material, and you must complete these assignments to earn a certificate of completion.

How can I apply the knowledge gained from this course?

The knowledge gained from this course can be applied in various domains such as software development, data science, content generation, image and video generation, enhancing creativity in design, and solving complex computational problems with generative models. Additionally, the skills learned can be utilized in academic research and industry projects focused on AI and machine learning.

Will I receive a certificate upon completing the course?

Yes, upon successfully completing the course requirements and passing the graded assignments, you will receive a certificate of completion, demonstrating your knowledge and skills in generative AI foundations.

How long will it take to complete the course?

The duration to complete the course will vary depending on the individual's pace of learning and the time dedicated to studying and completing assignments. However, the course is designed to be comprehensive yet flexible to accommodate different learning speeds.

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

What is the refund policy?

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy Opens in a new tab .

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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