Artificial Intelligence

Take a conceptual look at Artificial Intelligence, covering topics like handling of data, preprocessing, model selection and model evaluation.

Reed Courses Certificate of Completion

Digital certificate - Included

• Artificial Intelligence Course 6:00:24

  • 1: History of Artificial Intelligence and Early Systems 09:00
  • 2: History of Artificial Intelligence and Early Systems Preview 31:31
  • 3: Modern AI Systems and Applications 23:00
  • 4: Modern AI Systems and Applications 38:03
  • 5: Neuroscience and Neural Networks 32:57
  • 6: AI Explainability 41:30
  • 7: Data and Use Cases 36:11
  • 8: Python Libraries 27:34
  • 9: Preprocessing and ML Models 32:44
  • 10: Algorithms and Evaluation 28:36
  • 11: Statistics, Probabilities and Machine Learning Mathematics 30:58
  • 12: What the NET? 28:20

Diploma in Artificial Intelligence

1.History of Artificial Intelligence and Early Systems

Learn the influences of AI (Linguistics, Cognition, Gaming, Internet of Things, and Quantum Computing). Understand the theory behind Reinforcement Learning, Supervised Learning and Unsupervised Learning, and explore the history and development of AI.

2.Modern AI Systems and Applications

This lesson looks at the hardware and technological advances that made AI possible, and we'll understand how Artificial Intelligence is being used in mobility and autonomous vehicles, personalised medicine, retail and industry, and fintech.

3.Neuroscience and Neural Networks

Here we'll have an introduction to Neural Networks in AI. Explore the link between Cognitive Sciences and Neural Nets, differentiating between Artificial Narrow Intelligence (ANI) and General Artificial Intelligence (AGI). Develop the intuition behind Transfer Learning, Meta Learning, and Generative Adversarial Networks.

4.AI Explainability

Exploring fallacies of AI. Looking at biases in prediction and how they have already effected industry. We'll look at a brief overview on how to possibly combat Biases and irregularities in models.

5.Data and Use Cases

Understand how a Machine Learning Model can be implemented in Industry, as well as their different types of data. We'll also dive in to how AI & Machine Learning (ML) can benefit from ML operations.

6.Python Libraries

Python is one of the fundamental skills required by AI. Discover Python Libraries that are used, and what their functions are before knowing which libraries we'll use during this course and why.

7.Preprocessing and ML Models

The implications that can arise in data collection, and how problems are addressed, is important to understand. This leads to us to see how visualisation can assist in understanding the data.

8.Algorithms and Evaluation

Learn the importance of model tuning, and hyperparameter adjustment. We'll see where Evaluation is needed in the ML model and what the Evaluation process is useful for, along with a metric evaluation protocol in Regression and Classification Models. We'll also show how ML Algorithms are integrated into a model pipeline.

9.Statistics, Probabilities and Machine Learning Mathematics

Gain a basic understanding of evaluation metrics on regression, such as MSE, MASE, RMSE, and MAPE. Learn why vector and matrices play a pivotal role in ML. Finally, we'll see how calculus, and convolutions and Fourier Series have advanced Machine Learning.

10.What the NET?

Today we'll look into the capabilities of Natural Language Processing (NLP) and Computer Vision, exploring the current trends of Semantic Analysis, Named Entity Recognition and more. Understand the typical image acquisition process, as well as the flow of GANs, CNNs and RNNs.

The Upskillist AI course would appeal to a wide range of individuals who are interested in artificial intelligence, but specifically, the following types of people would be most drawn to it:

1. Beginners in AI and Machine Learning

• People who have little or no experience in AI but want to understand the basics.

• Individuals who are curious about how AI works and are looking for an entry-level course that breaks down complex topics into manageable lessons.

2. Professionals Looking to Upskill

• Professionals from various fields, such as marketing, finance, healthcare, or technology, who want to integrate AI into their careers.

• Those seeking to remain competitive in industries that are increasingly adopting AI tools and automation, and need to add AI knowledge to their skillset.

3. Students and Academics

• Students pursuing degrees in computer science, data science, or related fields who want supplemental material to strengthen their understanding of AI.

• Academics and researchers interested in understanding AI applications or expanding their teaching portfolios with AI-related content.

4. Entrepreneurs and Business Leaders

• Founders or managers looking to incorporate AI into their businesses to improve efficiency, decision-making, or customer experiences.

• Those who want to stay ahead of trends in innovation and technology by gaining a practical understanding of AI tools and their business applications.

5. Tech Enthusiasts

• People who are passionate about new technologies and enjoy staying up to date on cutting-edge developments.

• Hobbyists who are fascinated by AI, machine learning, and automation, and who enjoy learning new skills for personal growth.

6. Career Changers

• Individuals who want to transition into a career in AI, data science, or machine learning from unrelated fields.

• People who seek to pivot their career trajectory into more technology-driven roles, with a strong interest in AI’s job market opportunities.

7. Data Analysts and IT Professionals

• Data professionals who want to enhance their data analysis skills by integrating AI and machine learning models into their workflows.

• IT experts looking to expand their knowledge beyond traditional systems to include AI-powered solutions for problem-solving.

This course likely balances accessibility with depth, so it may attract individuals who are curious about AI’s potential in a practical, real-world context.

Access to online learning.

After completing the Upskillist AI course, several potential career paths may open up, depending on your skillset, and the level of expertise gained. Here are some key career options that can be pursued with AI knowledge:

1. Data Analyst

2. AI Specialist / AI Engineer

3. Machine Learning Engineer

4. AI Consultant

5. Business Intelligence (BI) Developer

6. Automation Engineer

7. AI Product Manager

Currently there are no Q&As for this course. Be the first to ask a question.