Now, that we know the basic fundamentals, definition and features of Machine Learning let’s dive into the world of Classic Machine Learning and then lets advance to Deep Learning.
This syllabus is going to cover topics like:
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CLASSICAL MACHINE LEARNING
- Prerequisites of Machine Learning
- Familiarization with the common modules used in the ML Pipeline
- Classification
- Regression
- Support Vector Machines
- K-Nearest Neighbors
- Decision Trees
- Clustering using the K-Means Algorithm
- Discussing on some other forms of ML
- Deep Learning
- Concepts of Supervised Deep Learning
- Intuition of Neural Networks
- Artificial Neural Networks
- Intuition of Convolutional Networks
- Deep Convolutional Neural Networks
- Intuition of a Recurrent Neural Network
- Recurrent Neural Networks and LSTM Cells
- Introduction to Unsupervised Deep Learning
- Self Organizing Maps – Intuition
- Self Organizing Maps
- Intuition of Boltzmann machines and Energy Based Models
- Intuition of Restricted Boltzmann Machines
- Deep Restricted Boltzmann Machines
- Intuition of Auto-Encoders
- Auto-Encoders
- Concepts of Supervised Deep Learning
- DISCUSSION OTHER FORMS OF MACHINE LEARNING
- Q-Learning
- Reinforcement Learning
- Generative Adversarial Networks
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We will be going through these algorithms one by one systematically. If possible, I will also attach code Snippets in case you want to see the Python Code to implement these models. To ensure that we do not make you feel isolated all the explanation will be very simple and straightforward, with little to no use of math. If you want the full in-depth explanation of these concepts I will link a few sources at the end of this journey!
This is going to be an Amazing journey! Honestly I cant wait to start this journey.
Hope you find this very interesting! Thank you and have a nice day 🙂
-MANAS HEJMADI
Synapse.AI 