Fundamentals of Machine Learning with Python Implementation.
- 3.5
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Brief Introduction
Learn Fundamentals of Machine Learning from scratch to make students well equipped with all basics and math involvedDescription
Hello there! Welcome to Fundamentals of Machine Learning with Python Implementation. There are many courses available out there for this domain but what makes us different is that the learning in this class is gradual. All the concepts are built from scratch to give students a fair idea of how various algorithms work in addition to live demonstrations
In this course, students will acquire a good understanding of basic concepts of machine learning. The course also introduces students to deep learning (neural nets) and also artificial intelligence. The concepts are developed from scratch to make students well equipped with all the basics and math involved with all machine learning algorithms
Some concepts we cover include
Various types of learning like supervised, unsupervised and reinforcement learning.
Various supervised learning algorithms like linear and logistic regression.
Clustering techniques.
A brief introduction to Neural Nets.
Parameter tuning, data visualization and accuracy estimation techniques
Reinforcement learning techniques like Q-learning and SARSA
Deciding which algorithm fits for a given problem
Knowing all of these techniques will give an edge to the developer in order to solve many real world problems with high accuracy.
Requirements
- Requirements
- Basics of python programming
- Linear algebra (understanding of matrices)
- Differential calculus (optional)
Knowledge
- Use Python for Data Science and Machine Learning
- Implement Machine Learning Algorithms
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
- Basics of Reinforcement Learning
- Linear Regression
- Logistic Regression
- Clustering
- SVM
- Neural Network Concept
- Random Forest
- PCA and SVD
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