Build Your Own Chatbot in Python

Course Overview

This course provides an introduction to Artificial Intelligence (AI) and its history, as well as how it has evolved over time. Participants will learn the fundamentals of Machine Learning (ML) and how it contributes to the rapid advancement of AI.

The course will provide an opportunity to experiment with three AI coding examples: a world clock, a calculator, and a chatbot. Participants will create their own chatbot, Jarvis, and train it with secret answers after it has passed all security checks.

Participants will learn the basics of Python programming language and how to use it to create a chatbot. They will also learn how to use the Natural Language Processing (NLP) library to create a conversational AI.

The course will cover topics such as:

• Introduction to Artificial Intelligence (AI)
• History of AI
• Machine Learning (ML)
• Natural Language Processing (NLP)
• Python Programming Language
• Creating a Chatbot
• Training a Chatbot
• Security Checks

At the end of the course, participants will have a working chatbot that they can use to interact with people. They will also have a better understanding of AI and ML, and how they can be used to create powerful applications.

[Applications]
This course provides a comprehensive introduction to artificial intelligence (AI), its history, and how it has evolved over time. Learners will gain an understanding of the fundamentals of machine learning (ML) and how it contributes to the rapid advancement of AI. Through hands-on activities, learners will have the opportunity to experiment with three AI coding examples: a world clock, a calculator, and a chatbot. Finally, learners will create their own chatbot, Jarvis, and train it with secret answers after it has passed all security checks.

Upon completion of this course, learners will have a better understanding of AI and ML, and will be able to apply their knowledge to create their own chatbot. They will also be able to use the skills they have learned to create other AI applications.

[Career Paths]
1. AI/ML Engineer: AI/ML Engineers are responsible for developing and deploying AI/ML models and algorithms. They are also responsible for designing and building AI/ML systems that can be used to automate tasks and improve decision-making. AI/ML Engineers are in high demand as the technology continues to evolve and become more widely adopted.

2. Data Scientist: Data Scientists are responsible for analyzing large datasets and uncovering insights and patterns. They use a variety of techniques, such as machine learning, natural language processing, and statistical analysis, to uncover insights from data. Data Scientists are in high demand as organizations look to leverage data to make better decisions.

3. Robotics Engineer: Robotics Engineers are responsible for designing, building, and programming robots. They use a variety of technologies, such as AI, ML, and computer vision, to create robots that can interact with their environment and perform tasks autonomously. Robotics Engineers are in high demand as robots become more prevalent in the workplace.

4. AI/ML Researcher: AI/ML Researchers are responsible for researching and developing new AI/ML algorithms and models. They use a variety of techniques, such as deep learning, reinforcement learning, and natural language processing, to develop new AI/ML models and algorithms. AI/ML Researchers are in high demand as organizations look to leverage the latest AI/ML technologies to improve their products and services.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and natural language processing. With the rapid advancement of AI, this degree path is becoming increasingly popular and in demand.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of AI systems and their applications. It covers topics such as machine learning, natural language processing, computer vision, robotics, and deep learning. This degree path is ideal for those who want to pursue a career in AI research and development.

3. Bachelor of Science in Data Science: This degree path focuses on the analysis and interpretation of data. It covers topics such as data mining, data visualization, predictive analytics, and machine learning. With the increasing demand for data-driven decision making, this degree path is becoming increasingly popular and in demand.

4. Master of Science in Machine Learning: This degree path focuses on the development of machine learning algorithms and their applications. It covers topics such as supervised and unsupervised learning, deep learning, reinforcement learning, and natural language processing. This degree path is ideal for those who want to pursue a career in machine learning research and development.