New Technology

Deep Learning Specialization

2024-09-10T06:23:00.000-07:00

What you'll learn

Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications

Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow

Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data

Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from DeepLearning.AI

Join Free: Deep Learning Specialization

Specialization - 5 course series

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

Applied Learning Project

By the end you’ll be able to:

• Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications

• Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow

• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning

• Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data

• Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering

Bias Variance Dichotomy in Data Science

2024-08-29T23:04:00.000-07:00

The concept here is that while adding complexity to the model might improve the fit, it need not improve the predictive accuracy on new data.

It is also referred to as Occam's Razor

300+ Python for iOS App Development Quiz

2024-08-24T08:55:00.000-07:00

Master iOS app development with Python through 300+ quizzes, explanations, Get ready for your next big opportunity!

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Description

About the Instructor

Welcome to "300+ Python for iOS App Development Quiz"! I’m Faisal Zamir, a Master’s degree holder in Computer Science with over 8 years of experience as a programmer, web developer, and educator. My extensive background ensures this course provides a robust preparation for your iOS app development journey.

Course Description

This comprehensive course is designed to thoroughly prepare you for iOS app development using Python with over 300 multiple-choice questions. Each question comes with the correct answer and a detailed explanation to reinforce your understanding. The questions are categorized to cover different levels of complexity and topics:

MCQs Categories:

Core Python Concepts for iOS: Fundamental Python concepts and their application in iOS app development.
Intermediate iOS Development with Python: Utilizing Python libraries and frameworks for more complex iOS functionalities.
Advanced iOS App Challenges: Tackling advanced topics such as performance optimization, security, and integration with native iOS components.

Why Important to Learn Python for iOS App Development and Prepare for Test/Exam

Python's simplicity and powerful libraries make it a great choice for iOS app development. Mastering Python for iOS allows you to build robust, efficient apps and gives you an edge in the competitive app development market. This course ensures you are well-prepared for any related tests or exams.

Money-Back Guarantee

This course offers a 30-day money-back guarantee. If you are not satisfied with the content, you can request a full refund within 30 days.

Direct Message to Instructor

For any inquiries or support, please reach out to me via the Udemy messaging system. I’m here to help you succeed.

Machine Learning Using Python

2024-08-24T08:45:00.000-07:00

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This course serves as an introduction to the field of machine learning with a focus on implementation using Python programming language. Machine learning is a branch of artificial intelligence that enables systems to learn from data and make predictions or decisions without being explicitly programmed. Python has emerged as one of the most popular programming languages for machine learning due to its simplicity, versatility, and a rich ecosystem of libraries such as scikit-learn, Mlxtend, Pandas, Seaborn, SciPy etc.

Throughout this course, students will explore fundamental machine learning concepts, algorithms, and techniques, and gain hands-on experience in implementing them using Python. The course will cover topics including:

1. Introduction to Machine Learning

2. Data Cleaning using Python

· Creating a Data Frame

· Describing the Data

· Navigating Data frames

· Selecting Row Based Conditionals

· Replacing Values

· Renaming Columns

· Finding The Minimum, Maximum. Sum, Average, and Count

· Finding Unique Values

· Handling Missing Values

· Deleting a Column

· Deleting a Row

· Dropping Duplicate rows

· Group Rows by Values and Time

· Looping over a Column

· Applying a Function Over All Elements in a Column

· Applying a Function to Groups

· Concatenating Data Frames

· Merging Data Frames

Handling Numerical Data

· Rescaling a Feature

· Standardizing a Feature

· Transforming Features

· Detecting Outliers

· Handling Outliers

· Deleting Observations with Missing Values

Handling Categorical Data

· Encoding Ordinal Categorical Features

· Encoding Dictionaries of Features

3. Plotting and exploring Numerical Data and Categorical Data

· Box Plot

· Histogram

· Scatterplot

· Cross Tabulations

4. Training and modelling the data

· Splitting a dataset into training and validation sets

· K-fold cross-validation

· Bootstrap Sampling

5. Dimensionality Reduction using Feature Extraction

· Reducing Features using PCA

· Reducing Features using LDA

· Reducing Features using NMF

6. Supervised Algorithms for Classification

· KNN

· Decision Tree

· Random forest

· Support Vector Machine

· Naive Bayes

· Logistic Regression

7. Improving Performance of the Model with Ensembling Methods

· Ada Boost

· XG Boost

8. Evaluating Performance of the Model for Classification

· Confusion Matrix

· Kappa Score

· F – measure

· Accuracy

· Precision

· Recall

· ROC Curve

9. Regression

· Linear Regression

· Logistic Regression

· Evaluation with R2 score

10. Unsupervised Algorithms

Clustering

· K-means

· K-Medoids

· Hierarchical

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Introduction to Generative AI

2024-07-24T23:49:00.000-07:00

Unlocking the Future: A Deep Dive into Coursera's "Introduction to Generative AI" Course

As artificial intelligence (AI) continues to evolve, one of its most exciting and innovative branches is generative AI. This field focuses on creating models that can generate new content, such as text, images, and music, which opens up a world of possibilities across various industries. For those interested in exploring this cutting-edge technology, Coursera's "Introduction to Generative AI" course offers a comprehensive and accessible entry point. Here’s a closer look at what this course entails and why it's worth your time.

What is Generative AI?

Generative AI refers to algorithms that can generate new data similar to the data they were trained on. Unlike traditional AI, which is often used for classification and prediction, generative AI can create new and unique content. This ability makes it incredibly valuable in fields like entertainment, design, marketing, and more.

Why Take the "Introduction to Generative AI" Course on Coursera?

This course is designed for anyone interested in understanding the fundamentals of generative AI, from students and professionals to AI enthusiasts. Here are some key reasons to consider enrolling:

Expert Instruction: The course is taught by industry professionals who have a deep understanding of generative AI, ensuring high-quality content and insights.
Comprehensive Curriculum: The course covers a wide range of topics, from basic concepts to advanced techniques, providing a thorough understanding of generative AI.
Hands-On Learning: Practical exercises and projects allow learners to apply theoretical concepts to real-world scenarios, enhancing their understanding and skills.
Flexible Learning: Hosted on Coursera, the course offers the flexibility to learn at your own pace, making it accessible for those with busy schedules.

Course Breakdown

The "Introduction to Generative AI" course is structured to provide a solid foundation in generative AI. Here’s an overview of the key topics covered:

1. Fundamentals of Generative AI

The course begins with an introduction to the basic principles of generative AI, including an overview of different types of generative models and their applications.

2. Generative Adversarial Networks (GANs)

GANs are one of the most popular and effective types of generative models. This section delves into the architecture and functioning of GANs, explaining how they can generate realistic images, videos, and more.

3. Variational Autoencoders (VAEs)

Another powerful generative model is the VAE. Learners will explore how VAEs work, their advantages, and how they can be used to generate new data.

4. Practical Applications of Generative AI

Understanding the theoretical concepts is crucial, but seeing how they are applied in real-world scenarios is equally important. This section covers various practical applications of generative AI, from creating art and music to generating realistic images and enhancing data privacy.

5. Ethical Considerations and Future Trends

The course also addresses the ethical implications of generative AI, including issues related to data privacy, security, and potential misuse. Additionally, learners will explore future trends and developments in the field.

Benefits of Completing the Course

By the end of the "Introduction to Generative AI" course, learners will:

Have a solid understanding of the fundamental concepts and techniques in generative AI.
Be able to implement basic generative models using popular AI frameworks.
Understand the practical applications and potential impact of generative AI.
Be aware of the ethical considerations and challenges associated with generative AI.
Earn a certificate from Coursera, which can enhance their professional credentials and career prospects.

Conclusion

Coursera’s "Introduction to Generative AI" course is an excellent opportunity for anyone interested in exploring this fascinating field. Whether you’re a student, a professional, or simply an AI enthusiast, this course provides the knowledge and skills needed to understand and apply generative AI techniques. As AI continues to advance and shape the future, staying informed and educated on the latest developments is essential. This course is a valuable step towards unlocking the potential of generative AI and staying ahead in the rapidly evolving tech landscape.

Join Free: Introduction to Generative AI

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

2024-07-24T23:40:00.000-07:00

Preparing for the Future: The Google Cloud Machine Learning Engineer Professional Certificate on Coursera

In an era where data drives decisions and machine learning (ML) propels innovation, acquiring expertise in these domains is crucial for tech professionals. For those aiming to enhance their skills and credentials in ML, the Google Cloud Machine Learning Engineer Professional Certificate on Coursera presents an ideal pathway. This comprehensive program is designed to equip learners with the knowledge and practical experience needed to excel in the field of machine learning using Google Cloud Platform (GCP). Here's a detailed look at what this certificate entails and why it's worth pursuing.

Why Choose the Google Cloud Machine Learning Engineer Professional Certificate?

This professional certificate is tailored for individuals looking to master the intricacies of ML engineering within the Google Cloud ecosystem. Here are some compelling reasons to consider this program:

Industry-Relevant Curriculum: The certificate program is designed by Google Cloud experts, ensuring that the content is up-to-date with the latest industry standards and practices.
Hands-On Learning: The program emphasizes practical experience, with numerous labs and projects that allow learners to apply theoretical concepts to real-world problems.
Expert Instructors: Courses are taught by experienced professionals who provide valuable insights and guidance, helping learners navigate complex topics effectively.
Flexibility and Accessibility: Hosted on Coursera, the program offers flexibility, allowing learners to progress at their own pace and access materials from anywhere.

Course Breakdown

The Google Cloud Machine Learning Engineer Professional Certificate consists of several courses, each focusing on different aspects of ML and GCP. Here’s an overview of what you can expect:

1. Google Cloud Platform Big Data and Machine Learning Fundamentals

This introductory course covers the basics of Google Cloud’s big data and machine learning services. Learners will gain an understanding of the core components and tools available on GCP, setting the foundation for more advanced topics.

2. How Google Does Machine Learning

This course provides insights into Google’s approach to ML, including the various phases of the ML lifecycle. Learners will explore best practices and strategies used by Google’s own engineers.

3. Launching into Machine Learning

Focusing on the practical application of ML concepts, this course covers key techniques such as feature engineering, model training, and evaluation. Learners will also work with TensorFlow, one of the most popular ML frameworks.

4. TensorFlow on Google Cloud

Building on previous courses, this course delves deeper into TensorFlow, exploring its integration with GCP. Learners will gain hands-on experience in deploying and scaling ML models using TensorFlow and Google Cloud services.

5. Advanced Machine Learning with TensorFlow on Google Cloud

This advanced course covers sophisticated ML techniques and their implementation using TensorFlow and GCP. Topics include deep learning, neural networks, and the use of ML APIs for various applications.

6. MLOps (Machine Learning Operations) Fundamentals

MLOps is an emerging field that focuses on the operational aspects of deploying and maintaining ML models. This course introduces learners to MLOps concepts, tools, and best practices, ensuring they can effectively manage ML projects from development to production.

Benefits of Completing the Certificate

Upon completing the Google Cloud Machine Learning Engineer Professional Certificate, learners will:

Have a thorough understanding of ML concepts and their application within the Google Cloud ecosystem.
Be proficient in using TensorFlow and GCP tools for developing, deploying, and managing ML models.
Gain practical experience through hands-on labs and projects.
Earn a certificate that can enhance their professional credentials and career prospects.

Conclusion

The Google Cloud Machine Learning Engineer Professional Certificate on Coursera is an excellent opportunity for tech professionals to advance their careers in the rapidly evolving field of machine learning. With its industry-relevant curriculum, hands-on learning approach, and expert instruction, this program equips learners with the skills and knowledge needed to excel in ML engineering. Whether you're looking to upskill, reskill, or simply stay ahead in your career, this professional certificate is a valuable investment in your future.

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Introduction to Generative AI Learning Path Specialization

2024-07-24T23:29:00.000-07:00

Exploring the Coursera Specialization: Introduction to Generative AI

As artificial intelligence (AI) continues to revolutionize various sectors, generative AI stands out as one of the most promising and intriguing branches. For those looking to dive deep into this cutting-edge field, the Coursera Specialization on "Introduction to Generative AI" offers a comprehensive and structured pathway. Here's an overview of what this specialization entails and why it's worth considering.

What is Generative AI?

Generative AI involves creating models that can generate new data resembling existing data. Unlike traditional AI, which focuses on classification and prediction, generative AI can create new content, such as text, images, music, and even video. This ability opens up endless possibilities in fields like entertainment, art, design, and more.

Why Choose Coursera’s Specialization?

Coursera's specialization is designed to cater to both beginners and those with some background in AI. Here are some key features:

Structured Learning Path: The specialization is divided into several courses, each building on the previous one. This structured approach ensures that learners gradually build their knowledge and skills.
Expert Instructors: The courses are taught by experts in the field, providing learners with high-quality content and insights from those actively working in AI research and development.
Hands-On Projects: Practical experience is crucial in understanding generative AI. The specialization includes various hands-on projects that allow learners to apply theoretical concepts to real-world scenarios.
Flexibility: Coursera’s platform allows learners to progress at their own pace, making it suitable for working professionals and students alike.

Course Breakdown

Let's take a closer look at the courses included in the specialization:

1. Introduction to Generative AI

This foundational course covers the basics of generative models, including key concepts and techniques. Learners will explore different types of generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

2. Deep Learning and Neural Networks for Generative AI

Building on the basics, this course delves into the deep learning techniques that power generative models. Topics include neural networks, training methods, and optimization techniques. Learners will also get hands-on experience with popular deep learning frameworks.

3. Advanced Generative Models

This course covers more advanced topics in generative AI, including the latest research and developments. Learners will explore cutting-edge models and techniques, such as transformers and diffusion models, and their applications in various domains.

4. Generative AI Applications and Case Studies

Understanding theoretical concepts is essential, but seeing how they are applied in real-world scenarios is equally important. This course provides case studies and examples of generative AI in action, across different industries and applications.

Benefits of Completing the Specialization

Upon completing the specialization, learners will have:

A solid understanding of the fundamentals and advanced concepts in generative AI.
Practical experience with implementing generative models using popular tools and frameworks.
Insights into the latest research and trends in the field.
A certificate from Coursera, which can be a valuable addition to a resume or LinkedIn profile.

Conclusion

The Coursera Specialization on "Introduction to Generative AI" is an excellent opportunity for anyone looking to explore this fascinating area of AI. Whether you're a student, a professional, or simply an AI enthusiast, this specialization provides the knowledge and skills needed to understand and apply generative AI techniques. As AI continues to evolve, staying informed and educated on the latest developments is crucial, and this specialization is a great way to do just that.

Join Free: Introduction to Generative AI Learning Path Specialization

Bracketology with Google Machine Learning

2024-07-24T23:21:00.000-07:00

What you'll learn

Use BigQuery to access the public NCAA dataset.

Prepare and transform the existing data into features and labels.

Use BQML to build a model based on the NCAA tournament dataset.

Join Free: Bracketology with Google Machine Learning

Learn, practice, and apply job-ready skills in less than 2 hours

Receive training from industry experts

Gain hands-on experience solving real-world job tasks

About this project

This is a self-paced lab that takes place in the Google Cloud console. In this lab you use Machine Learning (ML) to analyze the public NCAA dataset and predict NCAA tournament brackets.

Google AI Essentials

2024-07-24T23:17:00.000-07:00

What you'll learn

Use generative AI tools to help develop ideas and content, make more informed decisions, and speed up daily work tasks

Write clear and specific prompts to get the output you want - you’ll apply prompting techniques to help summarize, create tag lines, and more

Use AI responsibly by identifying AI’s potential biases and avoiding harm

Develop strategies to stay up-to-date in the emerging landscape of AI

Join Free: Google AI Essentials

There are 5 modules in this course

Google AI Essentials is a self-paced course designed to help people across roles and industries get essential AI skills to boost their productivity, zero experience required. The course is taught by AI experts at Google who are working to make the technology helpful for everyone.

In under 10 hours, they’ll do more than teach you about AI — they’ll show you how to actually use it in the real world. Stuck at the beginning of a project? You’ll learn how to use AI tools to generate ideas and content. Planning an event? You’ll use AI tools to help research, organize, and make more informed decisions. Drowning in a flooded inbox? You’ll use AI tools to help speed up those daily work tasks, like drafting email responses. You’ll also learn how to write effective prompts and use AI responsibly by identifying AI’s potential biases and avoiding harm.

After you complete the course, you’ll earn a certificate from Google to share with your network and potential employers. By using AI as a helpful collaboration tool, you can set yourself up for success in today’s dynamic workplace — and you don’t even need programming skills to use it.

Time Series Analysis: Forecasting and Control (Wiley Series in Probability and Statistics)

2024-07-13T08:31:00.000-07:00

Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject.

Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, the new edition covers modern topics with new features that include:

A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time series
An expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models
Numerous examples drawn from finance, economics, engineering, and other related fields
The use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecasting
Updates to literature references throughout and new end-of-chapter exercises
Streamlined chapter introductions and revisions that update and enhance the exposition

Time Series Analysis: Forecasting and Control, Fifth Edition is a valuable real-world reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields. The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.

Hard Copy: Time Series Analysis: Forecasting and Control (Wiley Series in Probability and Statistics)

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Introduction to Blockchain Technologies

2024-06-25T02:53:00.000-07:00

What you'll learn

The limitations of the Internet for business and economic activity, and how trust is established in a pre- and post-blockchain world

Terms such as miner, hash, nonce, proof-of-work, and public key cryptography, as well as the steps of a blockchain transaction

Seven design principles for blockchain technology

Ten challenges associated with implementing blockchain technology

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There are 5 modules in this course

In this first course of the specialization, we will discuss the limitations of the Internet for business and economic activity, and explain how blockchain technology represents the way forward. After completing this course, you will be able to explain what blockchain is, how it works, and why it is revolutionary. You will learn key concepts such as mining, hashing, proof-of-work, public key cryptography, and the double-spend problem. You’ll be able to describe seven design principles for blockchain technology, and the challenges facing the people developing it. You’ll also meet the players in the blockchain ecosystem, and consider your own role in stewarding the blockchain revolution.

Blockchain Security

2024-06-25T02:45:00.000-07:00

What Makes Blockchain Secure?

Decentralization: Unlike traditional centralized systems, blockchain operates on a decentralized network of nodes. This means that no single entity has control over the entire network, reducing the risk of centralized points of failure and attacks.

Cryptography: Blockchain utilizes advanced cryptographic techniques to secure transactions and data. Each transaction is encrypted and linked to the previous one through cryptographic hashes, ensuring data integrity and immutability.

Consensus Mechanisms: Blockchain networks use consensus algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), to validate transactions and add them to the ledger. These mechanisms prevent malicious actors from taking over the network, as they would need to control a majority of the network’s computational power or stake.

Transparency and Immutability: Every transaction on a blockchain is recorded on a public ledger that is immutable. Once a transaction is confirmed, it cannot be altered or deleted, providing a transparent and tamper-proof record.

Common Blockchain Security Threats

51% Attack: If a malicious actor gains control of more than 50% of the network's computational power or mining hash rate, they can potentially manipulate the blockchain. This includes reversing transactions, preventing new transactions, and double-spending.

Smart Contract Vulnerabilities: Smart contracts, self-executing contracts with the terms directly written into code, are prone to bugs and vulnerabilities. If exploited, these can lead to significant financial losses and breaches.

Sybil Attacks: In a Sybil attack, an adversary creates multiple fake identities (nodes) to gain a disproportionately large influence over the network, potentially disrupting consensus and decision-making processes.

Phishing and Social Engineering: As with any technology, blockchain users are susceptible to phishing attacks and social engineering. Hackers often target individuals to gain access to private keys and sensitive information.

Exchanges and Wallets: Centralized exchanges and wallets are prime targets for hackers. Security breaches in these platforms can result in significant losses for users, as they typically hold large amounts of cryptocurrency.

Enhancing Blockchain Security

Advanced Encryption: Implementing robust encryption methods ensures that data remains secure. Continuous advancements in cryptographic techniques are crucial to stay ahead of potential threats.

Auditing and Code Reviews: Regular audits and code reviews of blockchain platforms and smart contracts help identify and fix vulnerabilities before they can be exploited.

Multi-Factor Authentication (MFA): Enforcing MFA for access to wallets and exchanges adds an extra layer of security, making it harder for unauthorized users to gain access.

Education and Awareness: Educating users about common threats and best practices is essential. Awareness can significantly reduce the success rate of phishing and social engineering attacks.

Decentralized Exchanges (DEXs): Promoting the use of DEXs over centralized exchanges can reduce the risk associated with single points of failure. DEXs operate on smart contracts and do not hold user funds directly, enhancing security.

The Future of Blockchain Security

The evolution of blockchain security is ongoing, with researchers and developers constantly working on innovative solutions to address emerging threats. Some promising areas include:

Quantum-Resistant Algorithms: As quantum computing advances, traditional cryptographic methods may become vulnerable. Developing quantum-resistant algorithms is crucial for future-proofing blockchain security.

Zero-Knowledge Proofs: These cryptographic techniques allow for transaction verification without revealing sensitive information, enhancing privacy and security.

Interoperability Solutions: Enhancing the security of cross-chain transactions and communication between different blockchain networks is vital for a more connected and secure blockchain ecosystem.

Join Free: Blockchain Security

There are 5 modules in this course

This course introduces blockchain security, including a description of how the blockchain works at each level of the blockchain ecosystem. The instructor begins with the building blocks that create the structure of blockchain, the cryptography that it uses for security, and the role of hash functions in the blockchain and how they can be attacked. In the next module, the instructor describes what blockchain consensus is, why it’s needed, its underlying theory (Byzantine Fault Tolerance and Security via Scarcity), some of the common consensus algorithms, and the security issues inherent to each variant. The course progresses with a module that describes how blocks are created, the nodes and network that make up the blockchain ecosystem, and examples of various attacks that can be made on their security. Next the focus is on smart contracts and how their security can be compromised with vulnerabilities created by common programming errors during contract development, including in Ethereum, the most commonly used smart contract platform. Finally, the course wraps up with discussions of distributed ledger architectures that are alternatives to blockchain, second-level blockchain protocols, and advanced cryptography in blockchain.

Fintech: Foundations & Applications of Financial Technology Specialization

2024-06-25T01:14:00.000-07:00

What you'll learn

Explain crypto-currency and blockchain in financial contexts

Evaluate investment products using Modern Portfolio Theory

Identify the regulatory concerns presented by the most common financial technologies

Join Free: Fintech: Foundations & Applications of Financial Technology Specialization

Specialization - 4 course series

Wharton's Fintech Specialization is designed to introduce you to the fundamental building blocks of financial technologies and real-world applications through case studies of Wharton-led companies in the field. You'll learn the the essential components of technology-driven financial strategies, from complex regulations to cryptocurrency to portfolio optimization.

You'll also learn how modern investment strategies deploy technology to produce optimal results, explore the disruptive force of changing payment methods, analyze the changing regulatory landscape, and gain a deeper understanding of robo-advising, crowdfunding, peer-to-peer lending, and blockchain. By the end of this Specialization, you'll be able to make informed decisions about deploying financial technologies for yourself or for your business, giving you a competitive advantage in using the latest financial innovations.

Applied Learning Project

You'll learn the the essential components of technology-driven financial strategies, from complex regulations to cryptocurrency to portfolio optimization. You'll also learn how modern investment strategies deploy technology to produce optimal results, explore the disruptive force of changing payment methods, analyze the changing regulatory landscape, and gain a deeper understanding of robo-advising, crowdfunding, peer-to-peer lending, and blockchain.

By the end of this Specialization, you'll be able to make informed decisions about deploying financial technologies for yourself or for your business, giving you a competitive advantage in using the latest financial innovations.

Statistical and Machine-Learning Data Mining:: Techniques for Better Predictive Modeling and Analysis of Big Data

2024-06-24T09:18:00.000-07:00

Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.

What is new in the Third Edition:

The current chapters have been completely rewritten.

The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops.

Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP).

Includes SAS subroutines which can be easily converted to other languages.

As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

PDF Download: Statistical and Machine-Learning Data Mining:: Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition 3rd Edition by Bruce Ratner (Author)

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Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning)

2024-06-24T09:06:00.000-07:00

In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives.

Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:

Thoroughly updated material throughout

New chapters on boosting, adaptive regret, and approachability and expanded exposition on optimization

Examples of applications, including prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training, offered throughout

Exercises that guide students in completing parts of proofs

Hard Copy: Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning)

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Automate tasks and processes with Jira

2024-06-21T03:30:00.000-07:00

What you'll learn

Create automation rules with Jira using triggers, conditions and events

Automate business processes and completion tracking by adding sub-tasks and updating field values

Use branches and smart values to manage dependencies and email communication

Join Free: Automate tasks and processes with Jira

About this Guided Project

In this 2 and a half hours long guided project you will learn the basics of Jira automation. You will create automation rules using triggers, conditions and actions to auto assign tasks, apply sub tasks templates to reflect your business processed into your project management. You will add branches applying conditions based on relationships between issues so to automatically update their status depending on completion of their sub-tasks. Finally you will use smart values to interact with Jira information and optimize communication by sending automatic emails depending on events of your project management.

Software Testing and Automation Specialization

2024-06-21T03:17:00.000-07:00

What you'll learn

Distinguish between verification and validation describing the key differences between them.

Write automated functional tests for both front-end and back-end code

Measure the fault-finding effectiveness of a functional test suite using mutation testing.

Defend program correctness through the use of formal methods, specifically proof obligations and model checking.

Join Free: Software Testing and Automation Specialization

Specialization - 4 course series

This Specialization is intented for beginning to intermediate software developers seeking to develop knowledge and skill in implementing testing techniques and tools in the development of their projects. Through four courses, you will cover black-box and white-box testing, automated testing, web & mobile testing, and formal testing theory and techniques, which will prepare to you to plan and perform effective testing of your software.

Applied Learning Project

Learners will build test plans, test suites, and test analysis reports. Learners will develop properties and assertions in code to facilitate automated test generation. Learners will also create pre-conditions for methods to facilitate formal proofs of correctness.

Robotic Process Automation (RPA) Specialization

2024-06-21T03:12:00.000-07:00

What you'll learn

Learn the basic concepts of Robotic Process Automation

Develop familiarity and deep understanding of UiPath Platform

Develop the ability to independently design and create automation for business processes.

Prepare to become a Junior RPA Developer. This specialization also prepares you for UiPath Certified Professional - UiRPA.

Join Free: Robotic Process Automation (RPA) Specialization

Specialization - 6 course series

The Robotic Process Automation (RPA) specialization offers comprehensive knowledge and professional-level skills focused on developing and deploying software robots. It starts with the basic concepts of Robotic Process Automation. It builds on these concepts and introduces key RPA Design and Development strategies and methodologies, specifically in the context of UiPath products. A student undergoing the course shall develop the competence to design and develop automation solutions for business processes. This specialization also prepares you for UiPath Certified Professional - UiRPA exam.

Applied Learning Project

The practice exercises of the course consists of exercises mapped to the Theory portion. Each exercise will help the student practice and apply the skills learned in the Theory section. At the end of the specialization is an industry-level capstone project which will help the students to assimilate the knowledge and skills learnt and build an end-to-end automation solution for a business process.

Bitcoin and Cryptocurrency Technologies

2024-06-19T22:21:00.000-07:00

Cryptocurrencies have revolutionized the financial world, with Bitcoin being the pioneering force. Bitcoin, introduced in 2009 by an anonymous person or group known as Satoshi Nakamoto, has sparked a global movement towards decentralized digital currencies. This blog aims to explore the foundational technologies behind Bitcoin and other cryptocurrencies, including blockchain technology, cryptographic principles, and the broader implications of decentralized finance (DeFi).

1. Understanding Bitcoin

Bitcoin is a digital currency that allows peer-to-peer transactions without the need for intermediaries like banks. It operates on a decentralized network using blockchain technology. Each Bitcoin transaction is recorded on a public ledger called the blockchain, ensuring transparency and security.

Key Features of Bitcoin:

Decentralization: Unlike traditional currencies, Bitcoin is not controlled by any central authority, reducing the risk of censorship and manipulation.

Limited Supply: Bitcoin has a capped supply of 21 million coins, making it deflationary and potentially a store of value similar to gold.

Security: Bitcoin transactions are secured through cryptographic algorithms, making it nearly impossible to alter transaction data once it's added to the blockchain.

2. Blockchain Technology

The backbone of Bitcoin and most cryptocurrencies is blockchain technology. A blockchain is a distributed ledger that records transactions across many computers in such a way that the registered transactions cannot be altered retroactively.

How Blockchain Works:

Transaction Initiation: A transaction is requested.

Transaction Verification: The requested transaction is broadcast to a network of computers known as nodes.

Block Creation: Once the transaction is verified, it is combined with other transactions to create a new block of data for the ledger.

Proof of Work: The new block is then added to the blockchain after a complex algorithmic problem is solved, which requires computational power (this is known as mining in the case of Bitcoin).

Completion: The transaction is complete and permanently recorded in the blockchain.

3. Cryptographic Principles

Cryptocurrencies rely heavily on cryptography to secure transactions and control the creation of new units. Key cryptographic principles include:

Hash Functions: These are used to convert transaction data into a fixed-size string of characters, which appears random. Bitcoin uses the SHA-256 hashing algorithm.

Public and Private Keys: Each user has a public key, which is their address on the blockchain, and a private key, which is used to sign transactions and prove ownership.

Example:

Public Key: Think of it as an email address.

Private Key: Think of it as the password to access that email account.

4. Mining and Consensus Mechanisms

Bitcoin uses a consensus mechanism called Proof of Work (PoW) to secure the network and validate transactions. Miners compete to solve complex mathematical puzzles, and the first to solve the puzzle gets to add a new block to the blockchain and is rewarded with new bitcoins.

Alternatives to Proof of Work:

Proof of Stake (PoS): Instead of using computational power to validate transactions, validators are chosen based on the amount of cryptocurrency they hold and are willing to "stake" as collateral.

Delegated Proof of Stake (DPoS): A variation of PoS where stakeholders elect delegates to validate transactions and secure the network.

5. Beyond Bitcoin: Altcoins and DeFi

Bitcoin may have been the first, but it is not the only cryptocurrency. Thousands of alternative cryptocurrencies (altcoins) have been developed, each with unique features and uses.

Notable Altcoins:

Ethereum: Known for its smart contract functionality, allowing for decentralized applications (dApps).

Litecoin: Often referred to as the silver to Bitcoin's gold, with faster transaction times.

Ripple (XRP): Focuses on facilitating real-time, cross-border payments for financial institutions.

Decentralized Finance (DeFi):

DeFi is a movement aimed at creating an open-source, permissionless, and transparent financial system without intermediaries. DeFi applications include lending platforms, decentralized exchanges (DEXs), and stablecoins.

6. The Future of Cryptocurrencies

The future of cryptocurrencies is bright but uncertain. While they offer many advantages, including decentralization, security, and financial inclusion, they also face challenges such as regulatory scrutiny, scalability issues, and market volatility.

Potential Developments:

Scalability Solutions: Technologies like the Lightning Network for Bitcoin and Ethereum 2.0 aim to increase transaction throughput and reduce fees.

Regulatory Frameworks: Governments worldwide are working on creating regulations to ensure consumer protection without stifling innovation.

Mainstream Adoption: As more businesses and individuals adopt cryptocurrencies, their utility and value are likely to increase.

Conclusion

Bitcoin and cryptocurrency technologies represent a significant shift in how we think about money and financial transactions. By leveraging cryptography and blockchain technology, cryptocurrencies offer a decentralized, secure, and transparent alternative to traditional financial systems. As the technology and regulatory landscapes evolve, cryptocurrencies are poised to play a pivotal role in the future of finance.

Whether you are a seasoned investor, a tech enthusiast, or a curious newcomer, understanding the fundamentals of Bitcoin and cryptocurrency technologies is essential in navigating this rapidly changing landscape.

Join Free: Bitcoin and Cryptocurrency Technologies

Web3 and Blockchain Fundamentals

2024-06-19T22:15:00.000-07:00

What you'll learn

Explain the meaning of terms such as “Web3,” “token,” “smart contract,” “decentralized autonomous organization (DAO),” and “blockchain trilemma”

Describe seven principles for the design of blockchain-based systems, and seven challenge areas associated with implementing blockchain technology

Identify five questions one must consider when deciding whether blockchain is appropriate for a particular problem or use-case

Describe some real-world examples of companies that have integrated blockchain into their business models

Join Free: Web3 and Blockchain Fundamentals

There is 1 module in this course

There is a lot of hype around Web3 and blockchain. As with any disruptive technology, there are many competing sources of information. But if we look beyond the hype and mania—not to mention the fear, uncertainty, and doubt—we see something profound happening. We are witnessing one of the largest transformations of wealth in human history—from paper-based analog assets to digital ones. Blockchain represents the first native digital medium for value, and is foundational to the next generation of the Internet—also known as Web3.

In this short course you’ll learn what Web3 is, and how it differs from Web1 and Web2 in important ways. You’ll explore blockchain as the foundational technology for the Web3 era, and will learn about key concepts, such as digital assets (tokens), smart contracts, decentralized autonomous organizations (DAOs), and the blockchain “trilemma.” You will be able to describe seven principles for the design of blockchain-based systems, and seven challenge areas associated with implementing blockchain technology. You will be able to identify the questions one must consider when deciding whether or not blockchain is appropriate for a particular problem or use-case, and you will be able to describe some real-world examples of companies that have integrated blockchain into their business models.

This course is non-technical (i.e. no coding required) and is intended for a broad audience. The video material for these courses includes content from our “Blockchain Revolution Specialization” and “Web3 and Blockchain in Global Commerce Specialization,” which has been remixed and re-edited for the purposes of this short course.

Blockchain Revolution Specialization

2024-06-19T22:11:00.000-07:00

What you'll learn

Seven design principles for the ‘Blockchain Revolution’

Top 10 challenges for implementing blockchain technology

Terms such as miner, hash, nonce, consensus mechanism, public key cryptography, cryptoasset, smart contract, DApp, and self-sovereign identity

Blockchain use-cases within your industry, and business model decisions to assess feasibility

Join Free: Blockchain Revolution Specialization

Specialization - 4 course series

Blockchain is poised to transform every industry and managerial function—redefining the ways we transact online, share ideas, and manage workflows. It’s a new technology that every business professional needs to understand.

This four-course Specialization introduces you to the world of blockchain technology—explaining what blockchain is, how it works, and why it’s revolutionary. You will learn about various categories of cryptoassets, and the ways they can be transacted on a blockchain. You will learn how blockchain is disrupting business models and financial services, offering organizations new choices in how they create and manage value.

The Specialization is taught by Don Tapscott and Alex Tapscott, globally-recognized authorities on innovation and technology and authors of the best-selling book Blockchain Revolution. It also includes various industry experts and developers from the Ethereum Foundation, Grid Singularity, Keyless Technologies, and ResonanceX who will share their experiences within the blockchain ecosystem.

Additionally, you will gain access to ground-breaking research from the

Blockchain Research Institute

as well as the Blockchain Case Commons—a crowdsourced collection of blockchain applications and use-cases spanning multiple industries. Upon completion of this Specialization, you will produce a Blockchain Opportunity Analysis, in which you identify and evaluate a promising application of blockchain technology in your own industry.

Applied Learning Project

Upon completion of this Specialization, learners will produce a Blockchain Opportunity Analysis, in which you identify and evaluate a promising application of blockchain technology within your chosen industry. The goals of this project are twofold: One, it’s for you to identify a specific need or problem within an industry that can potentially be solved using blockchain technology. Two, it’s for you to investigate possible solutions to this problem, including how these solutions might be executed. You will accomplish different project milestones each week, and will be introduced to several tools to organize your findings. As an outcome of this project, you’ll walk away with a consolidated, peer-reviewed Blockchain Opportunity Analysis, which you can use to pitch your idea to your organization or even to potential investors.

Blockchain Basics

2024-06-19T22:07:00.000-07:00

What you'll learn

A learner will be able to recognize foundational concepts of blockchain, and apply these program concepts on the blockchain.

Build your subject-matter expertise

This course is part of the Blockchain Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Blockchain Basics

There are 4 modules in this course

This first course of the Blockchain specialization provides a broad overview of the essential concepts of blockchain technology – by initially exploring the Bitcoin protocol followed by the Ethereum protocol – to lay the foundation necessary for developing applications and programming. You will be equipped with the knowledge needed to create nodes on your personal Ethereum blockchain, create accounts, unlock accounts, mine, transact, transfer Ethers, and check balances.

You will learn about the decentralized peer-to-peer network, an immutable distributed ledger and the trust model that defines a blockchain. This course enables you to explain basic components of a blockchain (transaction, block, block header, and the chain) its operations (verification, validation, and consensus model) underlying algorithms, and essentials of trust (hard fork and soft fork). Content includes the hashing and cryptography foundations indispensable to blockchain programming, which is the focus of two subsequent specialization courses, Smart Contracts and Decentralized Applications (Dapps). You will work on a virtual machine image, specifically created for this course, to build an Ethereum test chain and operate on the chain. This hands-on activity will help you understand the workings of a blockchain, its transactions, blocks and mining.

Main concepts are delivered through videos, demos and hands-on exercises.

Blockchain Specialization

2024-06-19T22:00:00.000-07:00

What you'll learn

Understand smart contracts, a core idea and computational model of blockchain that enables automation, autonomy, scalability and transparency.

Have an understanding and working knowledge of the emerging blockchain technology.

How to design and program smart contracts and decentralized application.

How to think of innovative application models, leveraging the blockchain technology.

Join Free: Blockchain Specialization

Specialization - 4 course series

This specialization introduces blockchain, a revolutionary technology that enables peer-to-peer transfer of digital assets without any intermediaries, and is predicted to be just as impactful as the Internet. More specifically, it prepares learners to program on the Ethereum blockchain. The four courses provide learners with (i) an understanding and working knowledge of foundational blockchain concepts, (ii) a skill set for designing and implementing smart contracts, (iii) methods for developing decentralized applications on the blockchain, and (iv) information about the ongoing specific industry-wide blockchain frameworks.

The specialization covers a range of essential topics, from the cryptographic underpinnings of blockchain technology to enabling decentralized applications on a private Ethereum blockchain platform.

It is ideal for programmers and designers involved in developing and implementing blockchain applications, and anyone who is interested in understanding its potential.

Applied Learning Project

Learners will apply the concepts covered in the courses to deploy an instance of a blockchain and carry out peer-to-peer transactions, program and test smart contracts using Solidity language; and analyze, design and code a blockchain-based solution for decentralized applications.

IBM Full Stack Software Developer Professional Certificate

2024-06-18T03:06:00.000-07:00

What you'll learn

Master the most up-to-date practical skills and tools that full stack developers use in their daily roles

Learn how to deploy and scale applications using Cloud Native methodologies and tools such as Containers, Kubernetes, Microservices, and Serverless

Develop software with front-end development languages and tools such as HTML, CSS, JavaScript, React, and Bootstrap

Build your GitHub portfolio by applying your skills to multiple labs and hands-on projects, including a capstone

Join Free: IBM Full Stack Software Developer Professional Certificate

Professional Certificate - 12 course series

Prepare for a career in the high-growth field of software development. In this program, you’ll learn in-demand skills and tools used by professionals for front-end, back-end, and cloud native application development to get job-ready in less than 4 months.

Full stack refers to the end-to-end computer system application, including the front end and back end coding. This Professional Certificate covers development for both of these scenarios. Cloud native development refers to developing a program designed to work on cloud architecture. The flexibility and adaptability that full stack and cloud native developers provide make them highly sought after in this digital world.

You’ll learn how to build, deploy, test, run, and manage full stack cloud native applications. Technologies covered includes Cloud foundations, GitHub, Node.js, React, CI/CD, Containers, Docker, Kubernetes, OpenShift, Istio, Databases, NoSQL, Django ORM, Bootstrap, Application Security, Microservices, Serverless computing, and more.

After completing the program you will have developed several applications using front-end and back-end technologies and deployed them on a cloud platform using Cloud Native methodologies. You will publish these projects through your GitHub repository to share your portfolio with your peers and prospective employers.

This program is ACE® and FIBAA recommended—when you complete, you can earn up to 18 college credits and 6 ECTS credits.

Applied Learning Project

Throughout the courses in the Professional Certificate, you will develop a portfolio of hands-on projects involving various popular technologies and programming languages in Full Stack Cloud Application Development. These projects include creating:

HTML pages on Cloud Object Storage

An interest rate calculator using HTML, CSS, and JavaScript

An AI program deployed on Cloud Foundry using DevOps principles and CI/CD toolchains with a NoSQL database

A Node.js back-end application and a React front-end application

A containerized guestbook app packaged with Docker deployed with Kubernetes and managed with OpenShift

A Python app bundled as a package

A database-powered application using Django ORM and Bootstrap

An app built using Microservices & Serverless

A scalable, Cloud Native Full Stack application using the technologies learned in previous courses

You will publish these projects through your GitHub repository to share your skills with your peers and prospective employers.

DevOps, Cloud, and Agile Foundations Specialization

2024-06-18T02:52:00.000-07:00

What you'll learn

DevOps essential characteristics including culture, behavior, practices, tools, methodologies, technologies and metrics.

Core concepts of Cloud Computing, including its benefits, models, use cases, platforms, services, resources and opportunities.

Agile practices of small batches, minimum viable products, pair programming, behavior- and test-driven development.

Scrum methodologies to create a product backlog, write user stories, use the sprint planning process, and measure performance.

Join Free: DevOps, Cloud, and Agile Foundations Specialization

Specialization - 3 course series

DevOps is more than just a fusion of development and operations. It is the cultural change that organizations everywhere are adopting to become more agile and innovate at scale. It makes it easier to deliver software and services at the speed the market demands.

This specialization will equip you with a foundational understanding of DevOps, Cloud Computing, Agile, and Scrum and their transformational impact on businesses and approaches to software engineering

You will learn about the DevOps culture and its benefits, Cloud Computing fundamentals, the Agile philosophy, and Scrum methodology. You will familiarize yourself with the skills, technology, tools, processes, practices, and metrics to become a successful DevOps practitioner.

This specialization is suitable for both technical audiences - who want to either upskill with DevOps or start a career in this in-demand domain to become DevOps Engineers, Software Engineers, Application Developers, SREs, Cloud Specialists, etc., as well less technical audiences - including managers, executives, product managers, and project managers.

Applied Learning Project

The projects in these courses do not involve any coding and are designed for you to apply and demonstrate your grounding in DevOps and its principles.

You will:

examine a business case study and make your recommendations for transforming the organization to adopt DevOps;

create a cloud account and provision a service on cloud; and

develop an Agile plan with ZenHub by writing user stories, and simulating a Scrum sprint.