Showing posts with label Machine Learning. Show all posts
Showing posts with label Machine Learning. Show all posts

Friday 23 February 2024

Financial Machine Learning (Foundations and Trends(r) in Finance)

  


Financial Machine Learning surveys the nascent literature on machine learning in the study of financial markets. The authors highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

This survey is organized as follows. Section 2 analyzes the theoretical benefits of highly parameterized machine learning models in financial economics. Section 3 surveys the variety of machine learning methods employed in the empirical analysis of asset return predictability. Section 4 focuses on machine learning analyses of factor pricing models and the resulting empirical conclusions for risk-return tradeoffs. Section 5 presents the role of machine learning in identifying optimal portfolios and stochastic discount factors. Section 6 offers brief conclusions and directions for future work.

PDF: Financial Machine Learning (Foundations and Trends(r) in Finance)


Hard Copy: Financial Machine Learning (Foundations and Trends(r) in Finance)


Free Courses Machine learning for Finance 

Fundamentals of Machine Learning in Finance https://www.clcoding.com/2024/02/fundamentals-of-machine-learning-in.html

Python and Machine Learning for Asset Management 

https://www.clcoding.com/2024/02/python-and-machine-learning-for-asset_19.html

Guided Tour of Machine Learning in Finance https://www.clcoding.com/2024/02/guided-tour-of-machine-learning-in.html

Python and Machine-Learning for Asset Management with Alternative Data Sets https://www.clcoding.com/2024/02/python-and-machine-learning-for-asset.html

Python for Finance: Beta and Capital Asset Pricing Model https://www.clcoding.com/2024/02/python-for-finance-beta-and-capital.html



Monday 19 February 2024

Fundamentals of Machine Learning in Finance

 


Build your subject-matter expertise

This course is part of the Machine Learning and Reinforcement Learning in Finance 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: Fundamentals of Machine Learning in Finance

There are 4 modules in this course

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.  

A learner with some or no previous knowledge of Machine Learning (ML)  will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance.
Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

The course is designed for three categories of students:
Practitioners working at financial institutions such as banks, asset management firms or hedge funds
Individuals interested in applications of ML for personal day trading
Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance  

Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

Python and Machine Learning for Asset Management

 


What you'll learn

Learn the principles of supervised and unsupervised machine learning techniques to financial data sets  

Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes    

Utilize powerful Python libraries to implement machine learning algorithms in case studies    

Learn about factor models and regime switching models and their use in investment management    \

Join Free: Python and Machine Learning for Asset Management

There are 5 modules in this course

This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions.

The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. 

We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques. Then, we will see how this new insight from Machine learning can complete and improve the relevance of the analysis.

You will have the opportunity to capitalize on videos and recommended readings to level up your financial expertise, and to use the quizzes and Jupiter notebooks to ensure grasp of concept.

At the end of this course, you will master the various machine learning techniques in investment management.

Python and Machine-Learning for Asset Management with Alternative Data Sets

 


What you'll learn

Learn what alternative data is and how it is used in financial market applications. 

Become immersed in current academic and practitioner state-of-the-art research pertaining to alternative data applications.

Perform data analysis of real-world alternative datasets using Python.

Gain an understanding and hands-on experience in data analytics, visualization and quantitative modeling applied to alternative data in finance

Join Free: Python and Machine-Learning for Asset Management with Alternative Data Sets

There are 4 modules in this course

Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications. The approach of this course is somewhat unique because while the theory covered is still a main component, practical lab sessions and examples of working with alternative datasets are also key. This course is fo you if you are aiming at carreers prospects as a data scientist in financial markets, are looking to enhance your analytics skillsets to the financial markets, or if you are interested in cutting-edge technology and research as  they apply to big data. The required background is: Python programming, Investment theory , and Statistics. This course will enable you to learn new data and research techniques applied to the financial markets while strengthening data science and python skills.

Thursday 15 February 2024

The Nuts and Bolts of Machine Learning

 


What you'll learn

Identify characteristics of the different types of machine learning 

Prepare data for machine learning models 

Build and evaluate supervised and unsupervised learning models using Python

Demonstrate proper model and metric selection for a machine learning algorithm

Join Free: The Nuts and Bolts of Machine Learning

There are 5 modules in this course

This is the sixth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions. You’ll focus on the two main types of machine learning: supervised and unsupervised. You'll learn how to apply different machine learning models to business problems and become familiar with specific models such as Naive Bayes, decision tree, random forest, and more.  

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. 

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.  

By the end of this course, you will:

-Apply feature engineering techniques using Python
-Construct a Naive Bayes model
-Describe how unsupervised learning differs from supervised learning
-Code a K-means algorithm in Python 
-Evaluate and optimize the results of K-means model
-Explore decision tree models, how they work, and their advantages over other types of supervised machine learning
-Characterize bagging in machine learning, specifically for random forest models 
-Distinguish boosting in machine learning, specifically for XGBoost models 
-Explain tuning model parameters and how they affect performance and evaluation metrics

Wednesday 14 February 2024

Web App Development and Real-Time Web Analytics with Python: Develop and Integrate Machine Learning Algorithms into Web Apps

 


Learn to develop and deploy dashboards as web apps using the Python programming language, and how to integrate algorithms into web apps.

Author Tshepo Chris Nokeri begins by introducing you to the basics of constructing and styling static and interactive charts and tables before exploring the basics of HTML, CSS, and Bootstrap, including an approach to building web pages with HTML. From there, he’ll show you the key Python web frameworks and techniques for building web apps with them. You’ll then see how to style web apps and incorporate themes, including interactive charts and tables to build dashboards, followed by a walkthrough of creating URL routes and securing web apps. You’ll then progress to more advanced topics, like building machine learning algorithms and integrating them into a web app. The book concludes with a demonstration of how to deploy web apps in prevalent cloud platforms.

Web App Development and Real-Time Web Analytics with Python is ideal for intermediate data scientists, machine learning engineers, and web developers, who have little or no knowledge about building web apps that implement bootstrap technologies. After completing this book, you will have the knowledge necessary to create added value for your organization, as you will understand how to link front-end and back-end development, including machine learning.

What You Will Learn

Create interactive graphs and render static graphs into interactive ones

Understand the essentials of HTML, CSS, and Bootstrap

Gain insight into the key Python web frameworks, and how to develop web applications using them

Develop machine learning algorithms and integrate them into web apps

Secure web apps and deploy them to cloud platforms

Who This Book Is For

Intermediate data scientists, machine learning engineers, and web developers.

Hard Copy: Web App Development and Real-Time Web Analytics with Python: Develop and Integrate Machine Learning Algorithms into Web Apps

Monday 12 February 2024

Introduction to Calculus (Free Courses)

 


There are 5 modules in this course

The focus and themes of the Introduction to Calculus course address the most important foundations for applications of mathematics in science, engineering and commerce. The course emphasises the key ideas and historical motivation for calculus, while at the same time striking a balance between theory and application, leading to a mastery of key threshold concepts in foundational mathematics. 

Students taking Introduction to Calculus will: 

gain familiarity with key ideas of precalculus, including the manipulation of equations and elementary functions (first two weeks), 

develop fluency with the preliminary methodology of tangents and limits, and the definition of a derivative (third week),

develop and practice methods of differential calculus with applications (fourth week),

develop and practice methods of the integral calculus (fifth week).

Join Free: Introduction to Calculus

Sunday 4 February 2024

Ultimate Step by Step Guide to ChatGPT Using Python: 90 Day Plan to Make Passive Income with Generative AI (Ultimate Step by Step Guide to Machine Learning Book 4)

 


Unlock the Future of AI!

Delve into the world of Generative AI with Daneyal Anis' groundbreaking book, "The Ultimate Step by Step Guide to ChatGPT Using Python". If you've ever been intrigued by how machine learning, data science, and artificial intelligence can be harnessed for tangible results, this guide is your key.

In today's digital age, the fields of Artificial Intelligence (AI), Machine Learning (ML), and Data Science are not just buzzwords; they are the foundational pillars that drive innovations across industries. From big tech giants to emerging startups, AI-powered solutions are the backbone of breakthroughs.

Here's what you'll discover within this comprehensive guide:

How the union of Python, the most popular language in data science, and GPT is revolutionizing the tech space.

Deep dives into the power and potential of GPT - learning its strengths, nuances, and applications.

Strategies for monetizing your AI and ML skills, unveiling the golden opportunities that await in the AI space.

Building robust AI portfolios and utilizing automation tools for efficiency and scalability.

Crafting AI profiles, including creating dynamic chatbots using ChatGPT.

Navigating the ethical considerations and responsibilities in the AI domain.

Beyond just the knowledge, this guide is crafted to action. That's why Daneyal also offers an exclusive 90-Day Plan to make passive income using Generative AI, leading you from the theoretical to practical monetization of your skills. Plus, get exclusive access to an in-depth Step by Step Course for those wanting a hands-on learning experience.

Editorial Reviews

The Digital Era is here, and AI is at its forefront. Equip yourself with the knowledge, tools, and strategies to not only participate in this revolution but also to thrive and lead. With "The Ultimate Step by Step Guide to ChatGPT Using Python", your transformative journey in the realm of AI is set on a promising path.

Hard Copy: Ultimate Step by Step Guide to ChatGPT Using Python: 90 Day Plan to Make Passive Income with Generative AI (Ultimate Step by Step Guide to Machine Learning Book 4)

Thursday 1 February 2024

Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

 


Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more

Discover modern causal inference techniques for average and heterogenous treatment effect estimation

Explore and leverage traditional and modern causal discovery methods

Book Description

Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.

You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code.

Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms.

The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.

What you will learn

Master the fundamental concepts of causal inference

Decipher the mysteries of structural causal models

Unleash the power of the 4-step causal inference process in Python

Explore advanced uplift modeling techniques

Unlock the secrets of modern causal discovery using Python

Use causal inference for social impact and community benefit

Who this book is for

This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.

Table of Contents

Causality – Hey, We Have Machine Learning, So Why Even Bother?

Judea Pearl and the Ladder of Causation

Regression, Observations, and Interventions

Graphical Models

Forks, Chains, and Immoralities

Nodes, Edges, and Statistical (In)dependence

The Four-Step Process of Causal Inference

Causal Models – Assumptions and Challenges

Causal Inference and Machine Learning – from Matching to Meta-Learners

Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More

Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond

Can I Have a Causal Graph, Please?

Causal Discovery and Machine Learning – from Assumptions to Applications

Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond

Epilogue

Hard Copy: Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

 


This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

Learn applied machine learning with a solid foundation in theory

Clear, intuitive explanations take you deep into the theory and practice of Python machine learning

Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices

Book Description

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.

Why PyTorch?

PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.

You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).

This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

Explore frameworks, models, and techniques for machines to 'learn' from data

Use scikit-learn for machine learning and PyTorch for deep learning

Train machine learning classifiers on images, text, and more

Build and train neural networks, transformers, and boosting algorithms

Discover best practices for evaluating and tuning models

Predict continuous target outcomes using regression analysis

Dig deeper into textual and social media data using sentiment analysis

Who this book is for

If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.

Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.

Table of Contents

Giving Computers the Ability to Learn from Data

Training Simple Machine Learning Algorithms for Classification

A Tour of Machine Learning Classifiers Using Scikit-Learn

Building Good Training Datasets – Data Preprocessing

Compressing Data via Dimensionality Reduction

Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Combining Different Models for Ensemble Learning

Applying Machine Learning to Sentiment Analysis

Predicting Continuous Target Variables with Regression Analysis

Working with Unlabeled Data – Clustering Analysis

Hard Copy : Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition 3rd Edition

 


Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Purchase of the print or Kindle book includes a free eBook in the PDF format.

Key Features

Third edition of the bestselling, widely acclaimed Python machine learning book

Clear and intuitive explanations take you deep into the theory and practice of Python machine learning

Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices

Book Description

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

What you will learn

Master the frameworks, models, and techniques that enable machines to 'learn' from data

Use scikit-learn for machine learning and TensorFlow for deep learning

Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more

Build and train neural networks, GANs, and other models

Discover best practices for evaluating and tuning models

Predict continuous target outcomes using regression analysis

Dig deeper into textual and social media data using sentiment analysis

Who this book is for

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

Table of Contents

Giving Computers the Ability to Learn from Data

Training Simple Machine Learning Algorithms for Classification

A Tour of Machine Learning Classifiers Using scikit-learn

Building Good Training Datasets – Data Preprocessing

Compressing Data via Dimensionality Reduction

Learning Best Practices for Model Evaluation and Hyperparameter Tuning

Combining Different Models for Ensemble Learning

Applying Machine Learning to Sentiment Analysis

Embedding a Machine Learning Model into a Web Application

Predicting Continuous Target Variables with Regression Analysis

Working with Unlabeled Data – Clustering Analysis

Implementing a Multilayer Artificial Neural Network from Scratch

Parallelizing Neural Network Training with TensorFlow

Hard Copy: Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition 3rd Edition

Thursday 25 January 2024

Introduction to Machine Learning on AWS

 


What you'll learn

Differentiate between artificial intelligence (AI), machine learning, and deep learning. 

Select the appropriate AWS machine learning service for a given use case.

Discover how to build, train, and deploy machine learning models.

Join Free: Introduction to Machine Learning on AWS

There are 2 modules in this course

In this course, we start with some services where the training model and raw inference is handled for you by Amazon. We'll cover services which do the heavy lifting of computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training and virtual agents. You'll think of your current solutions and see where you can improve these solutions using AI, ML or Deep Learning. All of these solutions can work with your current applications to make some improvements in your user experience or the business needs of your application.

Mathematics for Machine Learning: Linear Algebra

 


Build your subject-matter expertise

This course is part of the Mathematics for Machine Learning 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: Mathematics for Machine Learning: Linear Algebra

There are 5 modules in this course

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally  we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.

At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

Wednesday 24 January 2024

Create Machine Learning Models in Microsoft Azure

 


What you'll learn

How to plan and create a working environment for data science workloads on Azure 

How to run data experiments and train predictive models

Join Free: Create Machine Learning Models in Microsoft Azure

There are 3 modules in this course

Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models.

This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure. You will learn how to run data experiments and train predictive models. In addition, you will manage, optimize, and deploy machine learning models into production.

From the most basic classical machine learning models, to exploratory data analysis and customizing architectures, you’ll be guided by easy -to-digest conceptual content and interactive Jupyter notebooks.

If you already have some idea what machine learning is about or you have a strong mathematical background this course is perfect for you. These modules teach some machine learning concepts, but move fast so they can get to the power of using tools like scikit-learn, TensorFlow, and PyTorch. This learning path is also the best one for you if you're looking for just enough familiarity to understand machine learning examples for products like Azure ML or Azure Databricks. It's also a good place to start if you plan to move beyond classic machine learning and get an education in deep learning and neural networks, which we only introduce here.

This program consists of 5 courses to help prepare you to take the Exam DP-100: Designing and Implementing a Data Science Solution on Azure. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at cloud scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure . Each course teaches you the concepts and skills that are measured by the exam.

Wednesday 17 January 2024

Managing Machine Learning Projects

 


Build your subject-matter expertise

This course is part of the AI Product Management 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: Managing Machine Learning Projects

There are 5 modules in this course

This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects.  The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems.  Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems.

At the conclusion of this course, you should be able to:

1) Identify opportunities to apply ML to solve problems for users
2) Apply the data science process to organize ML projects
3) Evaluate the key technology decisions to make in ML system design
4) Lead ML projects from ideation through production using best practices

MLOps | Machine Learning Operations Specialization

 


What you'll learn

Master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments.

Utilize Amazon Sagemaker / AWS, Azure, MLflow, and Hugging Face for end-to-end ML solutions, pipeline creation, and API development.

Fine-tune and deploy Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face.

Design a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.

Join Free: MLOps | Machine Learning Operations Specialization

Specialization - 4 course series

This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps

Through this series, you will begin to learn skills for various career paths:

1. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.

2. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.

3. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.

4. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.

Applied Learning Project

Explore and practice your MLOps skills with hands-on practice exercises and Github repositories.

1. Building a Python script to automate data preprocessing and feature extraction for machine learning models.

2. Developing a real-world ML/AI solution using AI pair programming and GitHub Copilot, showcasing your ability to collaborate with AI.

4. Creating web applications and command-line tools for ML model interaction using Gradio, Hugging Face, and the Click framework.

3. Implementing GPU-accelerated ML tasks using Rust for improved performance and efficiency.

4. Training, optimizing, and deploying ML models on Amazon SageMaker and Azure ML for cloud-based MLOps.

5. Designing a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.

6. Fine-tuning and deploying Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face. Creating interactive demos to effectively showcase your work and advancements.

Machine Learning Foundations for Product Managers

 


Build your subject-matter expertise

This course is part of the AI Product Management 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: Machine Learning Foundations for Product Managers

There are 6 modules in this course

In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and why it is applied.  To successfully manage an AI team or product and work collaboratively with data scientists, software engineers, and customers you need to understand the basics of machine learning technology.  This course provides a non-coding introduction to machine learning, with focus on the process of developing models, ML model evaluation and interpretation, and the intuition behind common ML and deep learning algorithms.  The course will conclude with a hands-on project in which you will have a chance to train and optimize a machine learning model on a simple real-world problem.

At the conclusion of this course, you should be able to:
1) Explain how machine learning works and the types of machine learning
2) Describe the challenges of modeling and strategies to overcome them
3) Identify the primary algorithms used for common ML tasks and their use cases
4) Explain deep learning and its strengths and challenges relative to other forms of machine learning
5) Implement best practices in evaluating and interpreting ML models

Tuesday 16 January 2024

Hands-on Machine Learning with AWS and NVIDIA

 


There are 4 modules in this course

Machine learning (ML) projects can be complex, tedious, and time consuming. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project.

Join Free: Hands-on Machine Learning with AWS and NVIDIA

This course is designed for ML practitioners, including data scientists and developers, who have a working knowledge of machine learning workflows. In this course, you will gain hands-on experience on building, training, and deploying scalable machine learning models with Amazon SageMaker and Amazon EC2 instances powered by NVIDIA GPUs. Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. Amazon EC2 instances powered by NVIDIA GPUs along with NVIDIA software offer high performance GPU-optimized instances in the cloud for efficient model training and cost effective model inference hosting.

In this course, you will first get an overview of Amazon SageMaker and NVIDIA GPUs. Then, you will get hands-on, by running a GPU powered Amazon SageMaker notebook instance. You will then learn how to prepare a dataset for model training, build a model, execute model training, and deploy and optimize the ML model. You will also learn, hands-on, how to apply this workflow for computer vision (CV) and natural language processing (NLP) use cases. After completing this course, you will be able to build, train, deploy, and optimize ML workflows with GPU acceleration in Amazon SageMaker and understand the key Amazon SageMaker services applicable to computer vision and NLP ML tasks.

Thursday 4 January 2024

Structuring Machine Learning Projects

 


Build your subject-matter expertise

This course is part of the Deep Learning 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:Structuring Machine Learning Projects

There are 2 modules in this course

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. 

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.

This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.
 
The Deep Learning Specialization is our 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. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

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