Machine Learning Algorithms Overview - What all exist out there?

From Regression to Transformers - A Guided Tour of Machine Learning Algorithms

Every time someone begins learning machine learning, they face a wall of jargon.

Support Vector Machines. Decision Trees. Neural Networks. Reinforcement Learning. Transformers.
And the classic confusion - what exactly is the difference between AI, ML, and DL?

In one of our recent lectures, I tried to break through the noise and give students a map of the machine learning landscape.

The point of the lecture was simple:
To answer one of the most important and most overlooked questions in ML learning – Where do all these algorithms fit in, and what do I really need to learn?

This was not about equations. It was not about implementation.
It was about orientation – helping students step back, see the full landscape, and figure out what matters to them.

We covered:

-The boundary between AI, ML, and Deep Learning
-Supervised learning (classification and regression)
-Unsupervised learning (clustering, dimensionality reduction)
-Reinforcement learning (agents, policies, rewards)
-Evaluation metrics (accuracy, precision, recall, F1, ROC)
-Ensemble methods (random forests, XGBoost)
-Deep learning models (MLPs, CNNs, RNNs, Transformers)
-Probabilistic models (Naive Bayes, HMMs, GMMs)
-Graph Neural Networks (GCN, GAT, message passing)
-And the real foundation beneath it all – linear algebra, probability, calculus, optimization, and programming

There was no attempt to simplify the field into a checklist. Machine learning is vast and fast-moving.
But the goal was to equip learners with a mental map - to know what exists, what does not, and what deserves their time.

The feedback has been humbling. Many learners had heard these terms scattered across courses and videos, but never had the big picture laid out for them.

If you are just starting your ML journey, or if you are mentoring someone who is, point them to foundational overviews like this before they drown in code notebooks and Kaggle leaderboards.

Because sometimes, the best way to go deep is to first zoom out.

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Here is an 11-phase roadmap to master the 5 horsemen of AI: ML - DL - CV - NLP - LLM

Stepping into AI as a career can be difficult, but a clear roadmap can make all the difference.

The only resource you need: Vizuara’s YouTube playlists.

Phase 1️⃣: Mathematical Foundations (40 hours)
Playlist: Foundations for ML: https://lnkd.in/gKz-eybU
-Why Begin Here: Grasp the basics- Linear algebra, Probability, Statistics, Calculus, Optimization, Programming fundamentals
-Commitment: 4 hrs/week for 8 weeks.

Phase 2️⃣: Machine Learning (40 hours)
📌Playlist: ML from scratch: https://lnkd.in/gn2dEcE2
-Why It’s Important: Practical, project-based learning to understand ML workflows.
-Commitment: 4 hrs/week for 10 weeks.

Phase 3️⃣: Decision Trees (20 hours)
📌Playlist: Decision Trees from Scratch: https://lnkd.in/g3cmj2BR
-Why It’s Useful: Master decision tree algorithms are the backbone of many ML models.
-Commitment: 4 hrs/week for 5 weeks.

Phase 4️⃣: Deep Learning (40 hours)
📌Playlist: Neural Networks from Scratch: https://lnkd.in/gj8kHe2T
-Why It Matters: Understand the mechanics of neural networks through implementation.
-Commitment: 5 hrs/week for 8 weeks.

Phase 5️⃣: Computer Vision (40 hours)
📌Playlist: Computer Vision from Scratch: https://lnkd.in/gixx8Dhn
-Why Learn This: CV is revolutionizing the healthcare, automotive, and manufacturing sectors.
-Commitment: 4 hrs/week for 10 weeks.

Phase 6️⃣: NLP (40 hours)
📌Playlist: NLP from Scratch: https://lnkd.in/g7tdcjQR
-Why Learn This?: Foundational NLP concepts are the backbone of modern LLMs
-Commitment: 5 hrs/week for 8 weeks.

Phase 7️⃣: [Advanced topic] Explainable AI (15 hrs)
📌Playlist: https://lnkd.in/gNEx3ghr
-Commitment: 3 hrs/week for 5 weeks.

Phase 8️⃣: [Advanced topic] Graph Neural Networks (25 hrs)
📌Playlist: https://lnkd.in/g3RCPS8e
-Commitment: 3 hrs/week for 5 weeks.

Phase 9️⃣: LLM (40 hours)
📌Playlist: Build LLM from Scratch: https://lnkd.in/gjcyfCcE
-Why Learn This?: From scratch knowledge trumps everything
-Commitment: 5 hrs/week for 8 weeks.

Phase 🔟: Hands-on LLMs (40 hrs)
📌Playlist: Hands-on LLMs: https://lnkd.in/gJQ7ryE4
-Why Learn This?: Useful for industrial projects
Commitment: 5 hrs/week for 8 weeks.

Phase 🔟: Hands-on LLMs (40 hrs)
📌Playlist: Hands-on LLMs: https://lnkd.in/gJQ7ryE4
-Why Learn This?: Useful for industrial projects
Commitment: 5 hrs/week for 8 weeks.

Phase 1️⃣1️⃣: Deep Seek from scratch (40 hrs)
📌Playlist: https://lnkd.in/gJGsxaDG
Commitment: 5 hrs/week for 8 weeks.

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✅Total Duration: 400 hours
✅Timeline: 12-14 months, balancing learning with practical application.
✅Outcome: Build foundational ML knowledge, gain practical skills, and stay ahead with advanced topics.

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If you are willing to spend time, this roadmap get you there.

Follow Vizuara’s YouTube channel for structured and beginner-friendly playlists: https://lnkd.in/g455AJVw

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