Introduction to Artificial Intelligence and Machine Learning Training
Introduction to Artificial Intelligence and Machine Learning is a 2-day course where participants learn the core principles and components of Artificial Intelligence and Machine Learning including recognizing real-world applications and use case for AL and ML technologies.
Artificial intelligence (AI), machine learning (ML) and deep learning (DL) give organizations a way to extract value out of the troves of data they collect, delivering business insights, automating tasks and advancing system capabilities.
These technologies mater to organizations because as businesses and other organizations undergo digital transformation, they’re faced with a growing tsunami of data that is at once incredibly valuable and increasingly burdensome to collect, process and analyze.
New tools and methodologies are needed to manage the vast quantity of data being collected, to mine it for insights and to act on those insights when they’re discovered.
Many companies are already using forms of AI or machine learning (ML) for everything from automation of manual processes to predicting and fulfilling customer demand.
Most companies no longer resist the use of AI/ML. But many organizations are still befuddled on the best way to utilize these technologies.
Experts believe the first step is for companies to identify and clarify the most profitable use cases in order to determine their future AI investment and development needs.
For example, take the financial sector. One widespread use of AI and ML is in fraud detection. Visa, Mastercard and PayPal (all US) are using machine-learning algorithms to analyze data on customer behavior captured over several decades.
Such analysis can detect oddities in account activities and identify fraudulent activity in mere milliseconds at any point in the transaction cycle. According to recent reports, AI and ML have been very successful in reducing fraud.
Another prominent use of AI is in the healthcare industry. AI is being used in healthcare facilities and pharmaceutical industries. Allocation of resources and drug discovery are two of the many ways AI is being used.
Diagnostics is another area with potential, with AI used to check patients’ symptoms against possible causes or to analyze scans. Early adopters include Chinese health apps such as Ping An’s Good Doctor and Chinese hospitals, particularly in Shanghai, which allegedly wants to become a base for healthcare AI.
Electric grids are also investing in AI.
Introduction to Artificial Intelligence and Machine Learning Training by Tonex
This comprehensive training course by Tonex is designed to provide participants with a foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML). Participants will explore the fundamental concepts, tools, and techniques underpinning AI and ML, gaining insights into their real-world applications. Through a blend of theoretical knowledge and hands-on practical exercises, this course equips individuals with the skills necessary to navigate the AI and ML landscape effectively.
Learning Objectives: Upon completing this course, participants will be able to:
- Explain the core principles and components of Artificial Intelligence and Machine Learning.
- Understand the key algorithms and models used in AI and ML applications.
- Recognize real-world applications and use cases for AI and ML technologies.
- Develop basic AI and ML models and perform data analysis using popular tools.
- Evaluate the ethical and social implications of AI and ML technologies.
- Be prepared for further advanced studies or professional applications in AI and ML.
Audience: This course is ideal for:
- Professionals seeking to enhance their knowledge of AI and ML.
- Software developers interested in AI and ML application development.
- Business analysts exploring AI and ML for data-driven decision-making.
- Data scientists and engineers looking to broaden their expertise.
- Managers and decision-makers wanting to understand the potential of AI and ML.
- Academics and enthusiasts eager to begin their journey into AI and ML.
Course Outline:
Introduction to AI and ML
- Understanding AI and ML concepts
- Historical context and evolution of AI and ML
- AI vs. ML: Key differences and commonalities
- Exploring the importance of AI and ML in today’s world
- AI and ML in various industries
- Ethical considerations in AI and ML
Basic Concepts and Terminology
- Supervised, unsupervised, and reinforcement learning
- Feature engineering and data preprocessing
- Model selection and evaluation
- Bias, fairness, and transparency in AI and ML
- Data collection, labeling, and cleaning
- Tools and platforms for AI and ML development
Machine Learning Algorithms
- Linear regression and logistic regression
- Decision trees and random forests
- Support vector machines
- Clustering algorithms (K-means, hierarchical)
- Neural networks and deep learning
- Natural language processing (NLP) and computer vision
Real-World Applications
- Predictive analytics and recommendation systems
- Fraud detection and anomaly detection
- Sentiment analysis and chatbots
- Autonomous vehicles and robotics
- Healthcare and medical applications
- AI and ML in finance and marketing
Hands-on Practical Exercises
- Data preprocessing and feature engineering
- Model training and evaluation
- Building a simple AI/ML project
- Working with AI/ML libraries (e.g., TensorFlow, scikit-learn)
- Deployment and monitoring of AI and ML models
- Debugging and fine-tuning models
Ethical and Social Implications
- Bias and fairness in AI and ML
- Privacy and security concerns
- Accountability and transparency in AI decisions
- Regulations and guidelines in AI and ML
- Future trends and developments in AI and ML
- Preparing for an AI and ML career
This course offers a well-rounded introduction to Artificial Intelligence and Machine Learning, equipping participants with the foundational knowledge and skills needed to embark on a successful journey in the world of AI and ML.