Artificial Intelligence Projects with Python
Artificial Intelligence Projects with Python, Learn Artificial Intelligence with 12 Machine Learning and Deep Learning Python Projects - 2021
What you'll learn
- You will practice Machine Learning and Deep Learning Algorithms by solving real life problems using latest version of libraries (July 2021)
- How to use Machine Learning and Deep learning algorithms in different difficulty level projects using Python
- You will make 12 very interesting and enjoyable Artificial Intelligence projects with easy to follow lectures
- Image Classificiation Implementation using Deep Learning
- Time Series Prediction Implementation suing Python and Keras Tensorflow
- Clustering, Regression and Classification Algorithms Implementation using Python
- IMDB Sentiment Analysis using Machine Learning
- Transfer Learning Implementation using InceptionResNetV2
- Artificial Neural Networks Implementation using Python
- Well known datasets used in Artificial Intelligence education
- You will learn how to build custom datasets for both Deep Learning and Machine Learning applications.
- NLP (Natural Language Processing) Implementation using Python
- You will learn Geographical Clustering using AI
- You will get the Digital Udemy Certificate after completing the course (can be used in your Linkedin page)
- All 12 downloadable projects include Python source code that is composed of Machine Learning & Deep Learning Algorithms and models
- You will do Image Recognition and Classification using Convolutional Neural Network (CNN) and Artificial Neural Network Algorithms.
Description
Welcome,
The AI industry has expanded massively over the last years and is expected to grow even further. Many of the major corporations employ AI-assisted technicians to solve problems. The results were surprising. The median salary of an AI engineer is $150,000 which is up to $171,765 per year. Companies need AI experts who can build and deploy scalable models to meet growing industry demands. There are various AI projects you can do to learn about the library.
In this course, we aim to specialize in artificial intelligence by working on 12 Machine Learning Projects and Deep Learning Projects at various levels (easy - medium - hard). Before starting the course, you should have basic Python knowledge. Our aim in this course is to turn real-life problems that seem difficult to do into projects and then solve them using latest versions of artificial intelligence algorithms (machine learning algortihms and deep learning algorithms) and Python(3.8). This course was prepared in August 2021.
We will carry out some of our projects using machine learning and some using deep learning algorithms. In this way, you will have a general perspective on artificial intelligence. When you complete the projects in our course, you will get a clear understanding of the basic working principles of Machine Learning software and Deep Learning algorithms and the difference between them.
In our course, we will use well known datasets that are widely used by high level education about Machine Learning as well as custom datasets. By doing our projects, you will master artificial intelligence concepts as well as learn these famous datasets. After completing the course, you will be able to easily produce solutions to the problems that you may encounter in real life.
In our Machine Learning Projects we will use Scikit-Learn Python library. In our Deep Learning Projects we will use Tensorflow and Keras libraries.
The course is composed of 12 Artificial Intelligence Projects (Machine Learning Projects & Deep Learning Projects):
Project #1: House Price Prediction using Machine Learning
Project #2: HR Salary Calculation using Machine Learning
Project #3: Handwritten Digit Recognition using Multiple Machine Learning Models
Project #4: Advanced Customer Segmentation using Machine Learning
Project #5: IMDB Sentiment Analysis Using NLP (Natural Language Processing)
Project #6: Building a Movie Recommendation System
Project #7: Predicting Diabetes using Artificial Neural Networks
Project #8: Image Classification using Convolutional Neaural Network and Artificial Neural Network Algorithms (Deep Learning)
Project #9: Airline Passenger(Time Series) Prediction using Keras LSTM (Deep Learning)
Project #10: San Francisco Crime Geographical Clustering using Machine Learning
Project #11: Image Classification (ImageNet Library) using Transfer Learning - Keras InceptionResNetV2 (Deep Learning)
Project #12: Military Aircraft (Satellite) Imagery Classification using Deep Learning (Custom Datasets)
Each project will be implemented by Python using Jupyter Notebook. Python source code of each project is included in relevant Udemy course section. You can download source codes for all projects..
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans. Leading AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its goals.
Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, computer vision and defence industry where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Deep learning is an artificial intelligence function that aims to imitate the human brain’s ability to process data and recognise patterns for learning and making decisions.
Here in this course you will find Artificial intelligence projects for beginners as well as Intermediate/Advanced Level Artificial Intelligence Projects. at the end of the course you will have a clear artificial intelligence definition in your mind and you will get the answer of the question "what is ai ?"
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Vibhav Deshpande
Rating: 5,0
Good Source Material and Easy to follow lectures
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"When I go visit different companies even at the top Silicon Valley companies, very often I see people trying to apply machine learning algorithms to some problem and sometimes they have been going at for six months. But sometimes when I look at what their doing, I say, I could have told you six months ago that you should be taking a learning algorithm and applying it in like the slightly modified way and your chance of success will have been much higher."
Andrew NG (Professor at Stanford University Department of Computer Science and Department of Electrical Engineering)
Who this course is for:
- Everyone who is interested in Artifical Intelligence with easy to follow lectures
- Everyone who is interested in Machine Learning, Deep Learning and Artificial Neural Networks
- Everyone who is interested in easy to follow lectures with fast practical example projects.
- It doesn't matter if you are a student, engineer, manager or medical doctor this course is designed in such a way that people from all walks of life can understand artificial intelligence.
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