Artificial Intelligence (AI) with Python for Beginners: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers

Artificial Intelligence with Python for Beginners

Artificial intelligence is the intelligence demonstrated by machines, in contrast to the intelligence displayed by humans.

This tutorial covers the basic concepts of various fields of artificial intelligence like Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms etc., and its implementation in Python.

Audience

This tutorial will be useful for graduates, post graduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner.

Prerequisites

We assume that the reader has basic knowledge about Artificial Intelligence and Python programming. He/she should be aware about basic terminologies used in AI along with some useful python packages like nltk, OpenCV, pandas, OpenAI Gym, etc.

Detailed course outline:

Introduction to AI

. Introduction to AI and Machine Learning.

. Overview on Fields of AI:

. Computer Vision.

. Natural Language Processing (NLP).

. Recommendation Systems.

. Robotics.

. Project: Creation of Chatbot using traditional programming (Python revision).

Understanding AI

· Understanding how AI works.

· Overview of Machine Learning and Deep Learning.

· Workflow of AI Projects.

· Differentiating arguments vs parameters.

· Project: Implementing functions using python programming (Python revision).

Introduction to Data Science

· Introduction to Data Science.

· Types of Data.

· Overview of DataFrame.

· Project: Handling DataFrame using python programming by learning various tasks including:

. Importing Dataset

. Data Exploration

. Data Visualization

. Data Cleaning

Machine Learning

· Overview on Machine Learning Algorithms with examples.

· Types of Machine Learning:

. Supervised

. Unsupervised

. Reinforcement

· Types of Supervised Learning:

. Classification

. Regression

· Project: Training and deploying machine learning model to predict salary of future candidates using python programming.