Fundamentals of Machine Learning with Python Implementation

Fundamentals of Machine Learning with Python Implementation
Video: .mp4 (1280×720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 2.7 GB

Learn Fundamentals of Machine Learning from scratch to make students well equipped with all basics and math involved

What you’ll learn

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

Requirements

Basics of python programming
Linear algebra (understanding of matrices)
Differential calculus (optional)

Description

Some concepts we cover include

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.

Who this course is for:

Anyone interested in Machine Learning.
Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Any computer science student hoping to broaden their skillset
Anyone interested in Machine Learning Including business leaders, managers, app developers, consumers – you!

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