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Big Data LDN
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Machine Learning
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Utilizes Wolfram Language to illustrate a powerful combination of approach and technology
Emphasizes practical experiences and demonstrations, making concepts relatable and applicable
Designed for professionals seeking to leverage AI for data-driven insights
Highlights potential barriers and challenges associated with AI adoption
Covers a diverse range of industries, demonstrating the broad applicability of AI
Instructors from Wolfram Research Technical Services provide industry expertise and real-world examples
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AI: Lessons Learned from the Front Line.
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Career center
Learners who complete AI: Lessons Learned from the Front Line will develop knowledge and skills
that may be useful to these careers:
AI Engineer
AI Engineer
An AI Engineer uses artificial intelligence (AI) to solve complex problems. This course offers a perfect fit for this career, as it provides direct guidance from real-world industry experiences. Understanding the challenges and complexities of AI implementation is crucial for success in this field. Also, learning Wolfram Language, which is popular in AI development, provides an added advantage.
AI Researcher
AI Researcher
An AI Researcher develops new methods and algorithms to advance the field of artificial intelligence. By understanding AI's limitations and complexities discussed in this course, one can avoid pitfalls and contribute more efficiently to the field. Additionally, knowledge of Wolfram Language, used in AI research, is an added bonus.
Data Scientist
Data Scientist
A Data Scientist creates and utilizes statistical models and machine learning algorithms to extract insights from complex data. This course can aid aspiring Data Scientists by providing knowledge about AI's hidden barriers and dangers. This knowledge equips one with the necessary precautions to establish a successful and efficient data science career. Experience with Wolfram Language can also prove beneficial, as this is one of the most popular programming languages used in data science.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, test, and implement machine learning algorithms and systems. Enrollment in this course can prove particularly useful, providing an in-depth insight into AI's potential pitfalls and how they can be avoided. Moreover, the course's emphasis on Wolfram Language will enhance one's programming skills necessary for success in this role.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst (Quant) uses mathematical and statistical models to analyze and predict financial markets. This course may be beneficial for aspiring Quants, providing an understanding of AI's role in financial analysis and risk management. Knowledge of Wolfram Language, which is used in quantitative finance, is also an advantage.
Actuary
Actuary
An Actuary evaluates and manages financial risks. This course may be useful for aspiring Actuaries, providing insights into AI's applications in risk assessment and modeling. Understanding these applications can enhance one's capabilities as an Actuary.
Consultant
Consultant
A Consultant provides expert advice and services to clients. This course may be useful for aspiring Consultants, providing a foundation in AI's applications across various industries. Understanding these applications can enhance one's capabilities as a Consultant.
Data Analyst
Data Analyst
A Data Analyst collects, transforms, and analyzes data to extract meaningful insights. This course may be useful for aspiring Data Analysts, providing a foundation in AI principles and techniques. Understanding how AI can enhance data analysis tasks can help one excel in this role.
Software Developer
Software Developer
A Software Developer designs, builds, and maintains software applications. Experience with Wolfram Language, a versatile programming language used in various industries, can be a valuable asset for Software Developers. This course also provides practical insights into real-world AI projects.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. While this course does not provide specialized statistical training, it does provide a solid foundation in AI's applications in statistical analysis. Understanding these applications can enhance one's capabilities as a Statistician.
Marketer
Marketer
A Marketer develops and executes marketing strategies to promote products or services. This course may be useful for aspiring Marketers, providing a foundation in AI's applications in marketing analytics and customer segmentation. Understanding these applications can enhance one's capabilities as a Marketer.
Product Manager
Product Manager
A Product Manager defines and manages the development and launch of products. While this course does not directly relate to the core responsibilities of a Product Manager, understanding AI's potential and limitations can be beneficial in evaluating AI-powered products and features.
Business Analyst
Business Analyst
A Business Analyst bridges the gap between business and IT, identifying and defining business needs and translating them into technical requirements. While this course doesn't directly relate to the core responsibilities of a Business Analyst, understanding the practical applications and challenges of AI can aid in comprehending the technical aspects of projects involving AI.
Salesperson
Salesperson
A Salesperson sells products or services to clients. While this course does not directly relate to the core responsibilities of a Salesperson, understanding AI's potential and limitations can be beneficial in identifying potential clients and tailoring sales pitches.
Risk Manager
Risk Manager
A Risk Manager identifies, assesses, and mitigates risks. While this course does not directly relate to the core responsibilities of a Risk Manager, understanding the practical applications and challenges of AI can aid in comprehending the technical aspects of AI-related risk management strategies.
For more career information including salaries, visit:
OpenCourser.com/course/q16mla/ai
Reading list
We haven't picked any books for this reading list yet.
Gentle introduction to machine learning, suitable for those with no prior knowledge of the field, with a focus on understanding the concepts rather than the math.
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Provides a comprehensive treatment of machine learning from a probabilistic perspective, covering a wide range of topics from Bayesian inference to deep learning.
Practical guide to machine learning for programmers, with a focus on using Python to build and deploy machine learning models.
Provides a balanced treatment of both statistical and machine learning methods, making it accessible to a wide audience.
Practical guide to machine learning with Python, covering a wide range of topics from data preprocessing to model evaluation.
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Comprehensive and authoritative reference on deep learning, covering a wide range of topics from neural networks to reinforcement learning.
Practical guide to machine learning for those with no prior experience, covering a wide range of topics from data preprocessing to model evaluation. It great hands-on tutorial to pick up skills in machine learning.
While not focused specifically on Machine learning, this book covers a broad range of topics in Artificial Intelligence including machine learning, and good companion to delve deeper into the theoretical and technical aspects of the field.
Offers a concise yet comprehensive introduction to machine learning, covering essential concepts and algorithms in just over 100 pages. It balances theory and practice, making it suitable for data professionals looking to expand their knowledge or prepare for interviews. It includes illustrations, models, and algorithms with Python examples. This book is excellent for gaining a broad understanding and serves as a valuable quick reference.
A highly practical book that guides readers through building intelligent systems using popular Python libraries. It starts with fundamental techniques like linear regression and progresses to deep neural networks. is ideal for those who prefer a hands-on approach with code examples and exercises. It is widely used as a textbook and reference for practitioners.
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Considered a foundational text in the field of deep learning, this book provides a comprehensive theoretical and conceptual understanding of neural networks and deep learning techniques. It covers essential mathematical prerequisites like linear algebra and probability. While theoretically oriented, it crucial resource for those wanting to delve deeply into the mechanics of deep learning and is often used in graduate-level courses.
Provides an accessible introduction to statistical learning methods, which form the basis of many machine learning algorithms. It focuses on concepts and applications rather than rigorous mathematical proofs, making it suitable for a broad audience with a statistics background. It is often used as a textbook for undergraduate and graduate courses and offers practical examples in R or Python.
A more advanced and theoretical counterpart to 'An Introduction to Statistical Learning,' this book provides a deep dive into the statistical underpinnings of machine learning. It valuable reference for researchers and practitioners seeking a thorough understanding of the algorithms. While mathematically rigorous, it is considered a classic in the field and is often used in graduate-level programs.
This comprehensive book covers both the theoretical and practical aspects of machine learning from a probabilistic perspective. It explores various algorithms and concepts rigorously, including Bayesian methods and neural networks. It well-regarded textbook for advanced undergraduate and graduate students and serves as a strong reference for researchers.
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Focuses on the practical aspects of building effective machine learning systems, offering guidance on making strategic decisions in ML projects. It is particularly valuable for those transitioning into or working as ML engineers or data scientists. It provides practical advice and best practices based on real-world experience.
Provides the essential mathematical background required for understanding machine learning algorithms, covering linear algebra, calculus, probability, and statistics. It is an excellent resource for students and professionals who need to solidify their mathematical foundations to better grasp the inner workings of ML models. It can be used as a prerequisite text or a companion resource.
Considered the standard textbook for reinforcement learning, this book covers foundational principles and real-world applications of RL. It is essential reading for anyone interested in this specific area of machine learning, which is crucial for developing agents that learn through interaction. It includes examples and connections to neuroscience.
Offers a practical, hands-on introduction to machine learning using the scikit-learn library in Python. It focuses on the practical aspects of applying ML algorithms and is suitable for data scientists and developers. It helps readers understand the core concepts and how to implement them effectively.
As the title suggests, this book provides a very basic and accessible introduction to machine learning for individuals with no prior background in coding, math, or statistics. It uses plain language and visuals to explain fundamental concepts and algorithms. This is an excellent starting point for complete newcomers to the field.
This book, written by the Stanford University professor and former Chief Scientist at Baidu, provides a clear overview of machine learning with lots of diagrams and hands-on practice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/q16mla/ai
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Big Data LDN
This course belongs to a
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Big Data LDN 2019 . It's comprised of 101 courses:
- The Database Is Only Half Done
- Your AI Is Wrong! Lessons from Decision-making with Imperfect AI
- Multiparadigm Data Science: The Next Frontier in Decision Making
- Achieving 100% Adoption of Analytics in the Enterprise
- Data Skills: How Mondi Does Digital
- Building a Modern Data Architecture for the Data Driven Enterprise
- Harnessing the Power of Data to Improve Public Health
- Real-time Data Analytics at Scale Using Cloud Services
- Quick and Easy Access to Data through a Logical Data Warehouse Environment
- The Promise of Data Mastering at Scale
- Research vs. Reality in AI: Would You Trust Your Model with Your Life?
- Modernise Your IT Infrastructure for AI
- Scaling Machine Learning at Holiday Extras
- Why Do Some Machine Learning Models Fail?
- AI: Lessons Learned from the Front Line (viewing)
- Machine Learning in Real-time: Predicting Taxi Fare in NYC
- How AI and Automated Machine Learning Will Disrupt the Organisational Alignment of Businesses
- Multi-model Powered Machine Learning
- ML in Production: Serverless and Painless
- Leveraging Data Virtualization to Maximize Advanced Analytics and Machine Learning Potential in Your Data Architecture
- Bring the Power of Watson to AWS and Azure: Watson Anywhere Powered by Cloud Pak for Data
- Modern Data Warehousing at Scale Using Azure Data Factory
- Balancing Performance, Scalability, and High Availability in Your Database Environment
- Enabling Microservices for Big Data On-demand in the Cloud
- From ETL to ELT: Building a Scalable Data Stack with SQL
- Data, Governance, and Freedom
- How to Get the Technology Stack Right for Your Business
- Data Modelling with Apache Cassandra
- How to Orchestrate Your ML Pipelines in Hybrid Cloud
- Full Stack Metadata Driven Thinking to Drive Business Agility
- Enhance Developer Experience Using OpenShift on Azure
- Fast Data for Faster Business
- Driving Faster, Smarter Actions on IoT Data with New Real-time Analytics Capabilities
- Introduction to Adept and How Big Data and Analytics Is Transforming the Connected Car Ecosystem
- Bridge to Cloud
- How to Train Your AI Models at Scale
- 99% of Your IoT Data Is Worthless, but Which 99%?
- Data Engineering in MagicLab: The Birth and Life of an Event
- RNA - Event Stream Processing Made Simple
- When Data Leads, Transformation Follows - A Look at Qlik's Data Integration Portfolio
- Saving Energy in Homes with a Unified Approach to Data and AI
- Building Stream Processing Applications with Apache Kafka Using KSQL
- Real-world, Real-time Digital Transformation
- Machine Data in Industry 4.0: How to Handle It Better
- Why CockroachDB in the Kindred Platform?
- The Hardest Part of AI & Analytics Is Not AI, It Is Data Management
- Effective Cloud Migration Strategies for Your Modern Data Applications
- Snowflake? Why Should I Care?
- The Future of Business Intelligence Isn't a Tool
- AI-driven Retail for the 21st Century
- The Next Generation of Data Architecture
- Modern Enterprise Data Engineering
- Taking Control of User Analytics with Snowplow
- Flex Your Data(Ops) Muscles
- You Love Big Data, so Does R
- Building Predictive Maintenance Solutions
- Quickly, Codelessly Connect Any Application to Any REST API
- Rethinking Data Warehouse Modernization in the Age of Hybrid Cloud/On-premise Data Warehouses
- From Waterfall to Agile – A Data Warehouse Modernisation Story
- Freeing up Engineering and Infrastructure Resources to Scale with DataOps
- Unit Testing with pytest
- Credit Card Fraud – Why the Database Matters
- Data Science at SEGA
- British Telecom: Data-driven Digital Transformation
- Using Cloud Solution Platforms to Drive Business Value and Cost Optimization
- Data Analytics & IoT Innovations at Bosch
- Converting the CDO to a Profit Centre
- Why Self-service Data Analytics Usually Fails & Ideas for Succeeding
- Governed Data Discovery
- Journey to Create a Great Analytic Culture
- Something Amazing for Everyone: Embracing Inclusive Design to Deliver Accessible Experiences
- Democratising Data – How We Built a Self-serve Data Platform
- Discover the “Why” You Didn't Know Existed
- Selecting Your Fantasy Premier League Team with Advanced Analytics
- Decouple Storage from Compute
- Data Skills: Building a Data Team Fit for a Data-driven Enterprise
- Consent, Compliance, and the Impact on Customer Experience
- AI & Communication: Meaningful Data from New Sources
- Real-time Personalised Customer Interaction at Scale
- Data: The Musical, with LW Theatres
- Self-service and Tableau Community at Lloyds Banking Group
- The Digital Hero’s Journey: Using Big Data to Uncover Business Value and Meaning at Work
- Using Location Intelligence to Place Solar Panels in Rwanda
- Embedding Intelligence into the Heart of Customer Operations
- The Art of Customer Conversations
- How to Build a Fully Autonomous Card Fraud Detection System
- At Last! The Real-time Customer Experience
- Data Privacy Mythbusting
- Demystifying Data – What They Don’t Tell You at Data School
- Optimizing Your Data Value Chain with Agile Data Management
- Data at SEGA Europe
- Keep Your Data Lake from Getting Hadooped
- Top 10 Privacy Tips & Tricks in Big Data
- Data Governance as a Customer Service
- 7 AI Quick Win Projects
- Delivering Value with Intelligent Data Governance
- The Culture Competition – Personalisation vs. Privacy
- DataOps for AI and Digital Transformation
- Big Data LDN: A GDPR Retrospective
- GDPR: A Sword Not a Shield
- Drive Business Growth While Satisfying GDPR and CCPA
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