Machine Learning
A structured, project-based path to learn machine learning from basics to deep learning, including NLP, ANN, CNN, RNN, and Transformers.
Learn from: the original creator. Original content published on YouTube. DigitalSkillX organizes these public resources into a structured learning path. DigitalSkillX does not claim ownership or partnership.
Now playing: Deep Learning Complete Course | Part 1| ANN implementation.
This learning path takes you from the foundations of machine learning through supervised and unsupervised learning, model tuning, and ensemble methods. You'll then explore natural language processing and deep learning architectures, including artificial neural networks, convolutional neural networks, recurrent neural networks, and Transformers. Each lesson includes clear explanations, coding examples, and practical projects using Python and Scikit-learn. The path is designed for beginners with basic Python and statistics knowledge, and it builds up to interview-ready concepts.
Beginners who want a structured free introduction before deeper practice.
Lesson 1: Complete Machine Learning Course for Beginners | Part 1- Foundation | Sheryians AI School
This first lesson walks through the complete foundation of a real-world machine learning project. It covers everything you need to know before building any model, including the overall workflow and key concepts. The instructor provides CSV files and code for hands-on practice.
Open on YouTubeLesson 2: Part 2 - Supervised Learning | Complete Machine Learning Course for Beginners | Sheryians AI School
This session dives deep into supervised learning, focusing on regression models, especially linear regression. It combines theory, intuition, and hands-on implementation to help you understand how regression works and how to apply it.
Open on YouTubeLesson 3: Part 3 - Supervised Learning| Classification Algorithms for Beginners | Sheryians AI School
This lesson covers classification algorithms in supervised learning. You'll learn what classification is and implement multiple powerful algorithms, with practical examples and coding.
Open on YouTubeLesson 4: Part 4 - Model Tuning, Ensemble & Unsupervised Learning | Full ML Course | Sheryians AI School
This session takes your ML skills further by covering model tuning, ensemble methods, and unsupervised learning. You'll learn how to improve model performance and build stronger models using advanced techniques.
Open on YouTubeLesson 5: Learn Complete NLP with Project (Bag of Words, Tf-idf) | For Beginners
This video introduces Natural Language Processing (NLP) using machine learning, without deep learning. It covers bag-of-words and TF-IDF, and includes a project with code and data available on GitHub.
Open on YouTubeLesson 6: Deep Learning Complete Course | Part 1| ANN implementation.
This is the first part of the deep learning course, focusing on the foundation of neural networks. It covers everything needed before building your first ANN or CNN, with code and datasets provided.
Open on YouTubeLesson 7: Deep Learning Complete Course | Part 2| CNN implementation.
This lesson dives into Convolutional Neural Networks (CNNs), the architecture that changed computer vision. You'll learn how CNNs work and implement them, with code and datasets available.
Open on YouTubeLesson 8: Deep Learning Complete Course | Part 3| RNN implementation.
This session covers Recurrent Neural Networks (RNNs), which are used for sequential and time-based data. After mastering ANN and CNN, this completes your deep learning foundation.
Open on YouTubeLesson 9: Deep Learning Complete Course | Part 4 | Transformers & Attention Mechanism Completely Explained
This video explores Transformers, the architecture behind modern AI and large language models. It explains attention, self-attention, and encoder-decoder models with clear intuition, and shows how models process long sequences and generate text.
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