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via Udemy |
Go to Course: https://www.udemy.com/course/machine-learning-projects-with-python3/
Certainly! Here is a comprehensive review and recommendation for the Coursera course on Artificial Intelligence, focusing on Machine Learning and Deep Learning Projects: --- **Course Review:** This Coursera course offers an immersive and practical journey into artificial intelligence (AI) through hands-on projects that cover a broad spectrum of applications. Designed for learners with basic Python and Machine Learning knowledge, the course emphasizes transforming real-world problems into AI solutions using a structured project-based approach. **Strengths:** - **Practical Focus:** With 12 diverse projects ranging from house price prediction to complex image and sound classification, the course ensures learners gain tangible skills applicable in industry scenarios. - **Project Variety:** The inclusion of projects applying various algorithms—linear regression, polynomial regression, clustering, NLP, neural networks, transfer learning, and custom dataset handling—provides a comprehensive understanding of AI tools and techniques. - **Use of Well-Known Libraries:** The course extensively uses popular libraries such as Scikit-Learn, TensorFlow, and Keras, preparing students with industry-standard skills. - **Progressive Complexity:** Projects are structured from easy to hard, helping learners build confidence and deepen their understanding gradually. - **Dataset Diversity:** The use of publicly available datasets like Kaggle’s IMDB reviews, the ImageNet library, and custom datasets (e.g., satellite imagery, sound signals) exposes students to real-world challenges. **Weaknesses:** - **Prerequisites:** A basic understanding of Python and Machine Learning is required, which might be a barrier for absolute beginners. - **Depth of Theory:** While the course emphasizes application, some students may desire deeper theoretical explanations of algorithms beyond the project scope. - **Pace and Volume:** The extensive number of projects may require a significant time investment, which could be demanding for some learners. --- **Recommendations:** This course is highly recommended for intermediate learners who already have foundational Python and Machine Learning skills and are eager to apply AI concepts practically. It is particularly suited for those looking to build a robust portfolio of AI projects, either for career advancement or research purposes. If you enjoy learning by doing and want exposure to a wide array of AI applications, this course will serve you well. It is also a great resource for developing hands-on experience with industry-standard libraries, datasets, and problem-solving techniques. **Ideal for:** - Aspiring Data Scientists and AI Engineers - ML and DL enthusiasts wanting practical project experience - Professionals seeking to strengthen their portfolio with diverse AI projects - Students aiming to understand real-world applications of AI algorithms --- **Conclusion:** Overall, this Coursera course offers a valuable, project-oriented pathway to mastering artificial intelligence. Its combination of practical projects, use of renowned datasets, and exposure to both machine learning and deep learning techniques make it an excellent choice for motivated learners seeking to turn AI theory into tangible solutions. If you are looking to build a solid foundation through application and want to work on real-life problems using Python, this course is highly recommended. --- Let me know if you'd like assistance with additional details or guidance!
Welcome, In this course, we aim to specialize in artificial intelligence by working on Machine Learning Projects and Deep Learning Projects at various levels (easy - medium - hard). Before starting the course, you should have basic Python and Machine Learning knowledge. Our aim in this course is to turn real-life problems into projects and then solve them using artificial intelligence algorithms and Python. 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 and Deep Learning Projects:- Project #1: House Price Prediction using Machine LearningIn this project we will build a artificial intelligence model that predicts house prices using sklearn multiple linear regression algortihm.- Project #2: Salary Calculation using Machine LearningIt is a tedious work to calculate each employee's salary according to employee's experience level. In this project we are going to build a machine learning model for exact calculation of employee salaries. Since most of salary values are non-linear, a simple linear function can not be used for this calculation process. Generally most of the companies have polynomial salary values for their employees. Therefore we will use polynomial linear regression algorithm for solution here.- Project #3: Handwritten Digit Recognition using Multiple Machine Learning ModelsIn 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 this project, you will see how you can combine machine learning models and combine several models to solve complex problems. You will have solved a problem that can be used in daily life (recognition of a handwritten text by a computer) using Artificial intelligence (AI).- Project #4: Advanced Customer Segmentation using Machine LearningIn this project, we will use a new and advanced segmentation library developed by the Massachusetts Institute of Technology (MIT). The customer data in our Customer Segmentation project, which is included in the entry and intermediate level projects, was simple and the K-Means clustering algorithm was sufficient for segmentation. But life is not that simple! When you have complex customer data, if you do clustering with K-Means, you may get erroneous results! Since the customer data in this project is complex data (both numeric and categorical) just like in real life, here we will use a special unsupervised learning algorithm instead of a standard model and divide our 2000 customers into groups with the latest artificial intelligence algorithms.- Project #5: IMDB Sentiment Analysis Using NLP (Natural Language Processing)With this Project, we will develop sentiment analysis software using the NLP concept. In this study, we will use the data set obtained from the Kaggle platform, a platform belonging to Google. Thanks to our artificial intelligence software that we will develop in this project, we will be able to automatically extract positive or negative comments from the English IMDB movie reviews that come with this data set. With this project, you will learn the concept of NLP in a very short time without drowning in theory.- Project #6: Predicting Diabetes using Artificial Neural NetworksIn this project we are goint to predict whether or not a patient has diabets. We are going to use a well known dataset from Kaggle: Pima Indians Diabetes Database. In this dataset we have some medical test results and statistical information of 768 patients. We will have two different Artificial Neural Network solutions for this project:We will build the simplest ANN model using only 1 neuronWe will build another model using 2 hidden layers and a total of 25 neurons- Project #7: Image Classification using Convolutional Neural Network and Artificial Neural Network Algorithms (Deep Learning)We will make a project that automatically recognizes and classifies thousands of different image files using deep learning and artificial neural network algorithms. We will use Tensorflow and Keras libraries to achieve this.- Project #8: San Francisco Crime Geographical Clustering using Machine LearningIn this project, we will perform geographic clustering using Geolocation information (Latitude & Longitude) using a data set created by the SFPD (San Francisco Police Department), which includes crimes committed in the city of San Francisco between 2003-2015. We will also learn to determine the optimal number of clusters (hyperparameter K-value) for this data set using the Elbow method. Then, we will display the geographic coordinates in our clustering results on a Python-based geographic map system. Finally, we will learn how to export this map we created to an HTML file.- Project #9: Image Classification (ImageNet Library) using Transfer Learning - Keras InceptionResNetV2 (Deep Learning)Transfer learning uses "knowledge gained in solving a problem" and applies it to a different but related problem. In Transfer Learning, we use a model that has been previously trained on a dataset and includes weights and biases that represent the properties of the dataset it was trained on. In this project, we will use the InceptionResNetV2 model, which has a pre-trained 164-layer advanced architecture and is pre-trained with an ImageNet dataset containing more than 1 million images.- Project #10: Military Aircraft (Satellite) Imagery Classification using Deep Learning (Custom Datasets)In this project, we will classify military aircraft images obtained from satellites (F-22 Raptor, Boeing B-52, A-10 Thunderbolt,.. etc.) using Deep Learning algorithms. In this project you will learn to create your own dataset and you will learn to use these customized datasets on pre-trained models.- Project #11: Sound Signal Processing for Deep Learning using Python (Custom Datasets) (Part - 1/2) In order to perform Sound Recognition and Classification with Python, the audio files must be in a format that can be used in Deep Learning algorithms. This project is essentially a pre-request project of our next project in our course, "Project#12 - Sound Classification using Deep Learning" Project. In this project we will process sound signals using Mel-Frequency Cepstral Coefficients (MFCC) algorithms and prepare audio for deep learning use. In this project you will learn how to prepare and process your own custom audio dataset for Deep Learning Training and Test operations.- Project #12: Sound Classification using Deep Learning (Part - 2/2)We will build a CNN (Convolutional Neural Network) Architecture with three Hidden Layers and 500 neurons in total (125-250-125) using Tensorflow and Keras libraries. We will use the pre-processed sound signals from previous project which has a dataset with a total size of 5.8 GB audio.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...