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Genre: eLearning
What you’ll learn:
Build real-world Machine Learning and AI projects using Python
Train and evaluate machine learning, deep learning, NLP, and computer vision models
Use TensorFlow, Scikit Learn, YOLO, and pre-trained models like ResNet50
Perform image classification, object detection, clustering, and sentiment analysis
Create practical AI pipelines for preprocessing, training, testing, and saving models
Want to learn Machine Learning and AI by actually building projects instead of spending hours watching theory-heavy lectures?
This course is designed for developers, students, and beginners who want to learn practical Artificial Intelligence and Machine Learning using Python through real-world projects.
Instead of focusing only on mathematics and theory, this course takes a hands-on approach where you will build complete machine learning and deep learning applications step by step.
You will learn how modern AI systems work while building projects related to:
- Car Price Prediction
- Lung Cancer Classification
- Customer Segmentation
- Image Classification
- Natural Language Processing (NLP)
- Object Detection Using YOLO
The course starts with the fundamentals of machine learning and gradually moves into deep learning, computer vision, pretrained neural networks, and natural language processing.
You will also learn how to preprocess datasets, create machine learning pipelines, evaluate models, and save trained models for future use.
What You’ll Learn
- Build machine learning models using Python
- Train regression and classification models
- Understand clustering using K Means
- Create preprocessing pipelines using Scikit Learn
- Train deep learning models using TensorFlow and Keras
- Use pretrained models like ResNet50
- Build NLP sentiment analysis projects
- Perform image and video object detection using YOLO
- Work with real-world datasets
- Evaluate and improve model performance
- Save and reuse trained machine learning models
Projects Included
- Car Price Prediction System
- Lung Cancer Classification Model
- Customer Segmentation Using K Means Clustering
- Fruit/Image Classification Using Deep Learning
- Image Classification Using ResNet50
- NLP Text Classification
- YOLO Image Object Detection
- YOLO Video Object Detection
Who This Course Is For
- Beginners interested in AI and Machine Learning
- Python developers wanting to learn AI
- Students learning Machine Learning
- Web and mobile developers exploring AI
- Developers wanting practical hands-on projects
- Anyone curious about Deep Learning and Computer Vision
Requirements
- Basic Python knowledge
- No prior Machine Learning experience required
- A computer with internet access
- Google Colab or Jupyter Notebook
Why Take This Course?
Many AI courses focus heavily on theory and mathematics, making it difficult for beginners to stay engaged.
This course focuses on practical learning by building projects from day one.
By the end of the course, you will understand how real-world AI and Machine Learning systems are built and gain the confidence to continue building your own projects.
Who this course is for:
Beginners interested in Artificial Intelligence and Machine Learning
Python developers wanting to build AI-powered applications
Students learning Machine Learning, Deep Learning, and Computer Vision
Web and mobile developers exploring practical AI projects
Anyone who prefers hands-on learning over theory-heavy courses
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