If you're looking to specialize in artificial intelligence, the Artificial Intelligence Projects with Python course is an excellent option. By working on seven machine learning projects and seven deep learning projects at various levels (easy, medium and hard), you will gain a clear understanding of the basic working principles of machine learning software and deep learning algorithms and the difference between them.
Learn by Doing
This course provides plenty of opportunities to put your newly acquired knowledge to work. The course is composed of 14 artificial intelligence projects - Machine Learning Projects and Deep Learning Projects - that make use of well-known datasets as well as custom datasets. By completing these projects, you will gain a mastery of artificial intelligence concepts and a better understanding of the famous datasets that accompany them.
In Project #1, House Price Prediction using Machine Learning, you'll build an artificial intelligence model that predicts house prices using Scikit-Learn's multiple linear regression algorithm. The Salary Calculation project challenges you to build a machine learning model for calculating employee salaries. Here, we'll use polynomial linear regression algorithm for seamless calculation process.
Project #3 presents the opportunity to get creative as, In this project, we will implement a software that recognizes and makes sense of the objects in the photograph by using multiple Machine Learning Models together, thanks to the advanced concepts of machine learning algorithms you will learn in this very project.
Delve Deeper with Deep Learning
Of course, no artificial intelligence course would be complete without a look into deep learning projects. Artificial Intelligence Projects with Python takes your knowledge a step further by presenting seven deep learning projects leveraging TensorFlow and Keras libraries.
Project #8 will have you building a project that automatically recognizes and classifies thousands of different image files using deep learning and artificial neural network algorithms. Meanwhile, Project #9 will challenge you to use the airline passenger dataset to produce a solution using the LSTM model available in Keras, a great example of efficient deep learning to solve time-series problems in general.
Customization and Optimization
The final 3 projects of the course are extremely unique and stand out from other courses. They are designed to equip you with highly specialized skills that will make your work endeavours more efficient than before.
Project #10 will charge you with performing geographic clustering using Geolocation Information using a dataset created by the SFPD(San Francisco Police Department), which showcases how sometimes standard models may not show results, and the tricks you can use to ensure more accurate results.
In Project #11, Image Classification (ImageNet Library) Using Transfer Learning - Keras InceptionResNetV2 (Deep Learning) you will be leveraging transfer learning concept which uses "knowledge gained in solving a problem" and applies it to a different problem, we use a model that has been previously trained on a dataset and includes weights and biases that represent the dataset. Here, we will use the InceptionResNetV2 model, which is advanced in both architecture and the pre-training of an ImageNet dataset containing more than 1 million images.
In Project #14, Sound Classification Using Deep Learning we will build an advanced CNN(Convolutional Neural Network) Architecture using TensorFlow and Keras libraries, with three Hidden Layers and 500 neurons in total using pre-processed sound signals from previous project(Project #13), which has a dataset with a total size of 5.8 GB audio.
Is This Course Beginner-Friendly?
Yes, it is! Whether you are a beginner or a professional, this program is designed to cater to individuals of all skill levels.The course will cover topics like Machine Learning (ML), Deep Learning (DL), Linear Regression, Customer Segmentation, Natural Language Processing (NLP), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Time Series Prediction, Sentiment Analysis, Image Classification, Geographical Clustering, Sound Signal Processing for Deep Learning, and Audio Classification with Deep Learning.
To take on this course, you will need to have basic knowledge of Python. So, if you're just starting out, don't worry! There are plenty of online resources available to help you get the basic information you need to get started with Python.
Course Details
The Artificial Intelligence Projects with Python course has a course rating of 4.29053 on major learning platforms with a significant quantity of 67 reviews. The course is packed with 14 comprehensive projects, helping you to master artificial intelligence using python. Every project is presented with complete python source code available, and you can download the source codes for each of them.
Each project is implemented using Jupyter Notebook, and you'll find everything you need to know about artificial intelligence after completing this course. At the end of the course, you'll have a clear artificial intelligence definition stuck in your mindset and give you the perfect answer for questions like; "What is AI?" or "What are Machine Learning/Deep Learning?"
Concluding Thoughts
Regardless of your skill level, the Artificial Intelligence Projects with Python course offers a unique opportunity to learn practical applications of artificial intelligence. The fourteen projects provide real-world challenges that are not only engaging but far-reaching, providing skills that can be applied to your work endeavors. Don't hesitate to enroll today and gain proficiency in artificial intelligence concepts.
