Welcome to the world of Machine Learning and Deep Learning, where you have the opportunity to make your mark by building intelligent but straightforward programs that perform complex tasks. If you're already a data lover or want to learn more about data science and how to work on Machine Learning and Deep Learning projects, then you're in the right place to kick-off your learning journey with 'Machine Learning & Deep Learning Projects for Beginners 2023'.
What is this course about?
The course is taught by Vijay Gadhave, an experienced instructor in the data science field. It is a beginner-friendly course that takes you from the basics of Machine Learning and Deep Learning to the advanced level, where you will be working on 23 exciting projects in various categories. The projects span Regression, Classification, Clustering, ANN, CNN, RNN, and Transfer Learning. The course uses Google Colab Notebook for coding, enabling everyone to start coding without installation.
What makes this course unique?
One of the standout features of this course is its project-based approach, which means you learn by doing. Throughout the course, you will have a chance to work on exciting real-world projects that relate to different aspects of data science. This way, you will be able to apply the concepts you learn directly, which makes it easy to understand and remember them. What's more, the course has an aggregate rating of 4.65709, indicating that it offers great value and is highly regarded by fellow students.
What projects are included in this course?
The total of 23 projects included in this course spans a wide range of topics. Some of the projects you will work on include:
- Breast Cancer Detection
- Customer churn rate prediction
- Medical insurance premium prediction
- House price prediction
- E signing of customers based on financial data
- Credit card fraud detection
- Employee Attrition Prediction
- Customer Segmentation
- Used Car Price Prediction
- Restaurant Reviews Classification
- Multiclass image classification with ANN
- Binary Data Classification with ANN
- Object Recognition in Images with CNN
- Binary Image Classification with CNN
- Digit Recognition with CNN
- Breast Cancer Detection with CNN
- Predicting the Bank Customer Satisfaction with CNN
- Credit Card Fraud Detection with CNN
- IMDB Review Classification with RNN - LSTM
- Multiclass Image Classification with RNN - LSTM
- Google Stock Price Prediction with RNN and LSTM
- Transfer Learning for Cats and Dogs Classification
- Movie Review Classification with NLTK
What do you learn from this course?
The course covers a range of topics, from Regression, Classification, and Clustering Projects in Machine Learning to text projects and NN, CNN, RNN, and Transfer Learning Projects. You will learn how to build Neural Networks from scratch, understand Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks, how to build neural networks with LSTM and GRU. This course also focuses on Transfer Learning and Natural Language Processing by doing a text classification project.
Who can take this course?
The Machine Learning & Deep Learning Projects for Beginners 2023 course is beginner-friendly and is open to anyone who is interested in data science and machine learning. The course offers hands-on experience with different projects, guiding you through the learning process. It is ideal for those who wish to grasp complex concepts easily and learn through practical examples.
The Bottom Line
If you're interested in taking your first step towards data science and Machine Learning, then 'Machine Learning & Deep Learning Projects for Beginners 2023' is the course for you. The projects span different categories, allowing you to gain hands-on experience in a wide range of topics in data science. The course is a beginner-friendly, project-based approach and has great reviews from previous students.
Enroll in the course today and embark on a journey to gain practical knowledge of Machine Learning and Deep Learning via intuitive projects. The time to start your journey in Machine Learning is now.
