Hands On Training

From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase

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QuickStart

Course Features

Duration

20 hours

Delivery Method

Online

Available on

Lifetime Access

Accessibility

Desktop, Laptop

Language

English

Subtitles

English

Level

Beginner

Teaching Type

Self Paced

Video Content

20 hours

Course Description

Let' let's first get an idea of what Machine Learning, NLP, and Python are. Machine learning, an application of artificial intelligence (AI), is a method that allows systems to learn from their experience and make improvements. Machine learning is the creation of computer programs that can access data to learn for themselves. Neuro-linguistic programming (NLP), a method of communication, personal development and psychotherapy, was developed by Richard Bandler in California, United States, in the 1970s.

Python is an object-oriented, high level programming language that can be interpreted and has dynamic semantics. Its high-level, built-in data structures combined with dynamic binding and dynamic typing make it attractive for Rapid Application Development. This course will teach students about Machine Learning, Natural Language Processing using Python, Sentiment analysis, and Mitigating Overfitting through Ensemble Learning.

The average annual salary for Big Data Professionals is $69 870

Course Overview

Alumni Network

International Faculty

Case Based Learning

Post Course Interactions

Case Studies,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory

Working knowledge of Python would be helpful if you want to run the source code that is provided

What You Will Learn

A Few Useful Things to Know About Overfitting

Association Detection

Clustering as a form of Unsupervised learning

Decision Trees

Dimensionality Reduction

Natural Language Processing and Python

Random Forests

Recommendation Systems

Regression as a form of supervised learning

Sentiment Analysis

Solving Classification Problems

Target Students

Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning

Engineers who want to understand or learn machine learning and apply it to problems they are solving

Course Content

Expand all sections

Module 1: Introduction

Module 2: Jump right in : Machine learning for Spam detection

Module 3: Solving Classification Problems

Module 4: Clustering as a form of Unsupervised learning

Course Instructors

Brian Hernandez

Web development Instructor

Brian Hernandez has been in the development field for over a decade. Brian works extensively with Full Stack Web Development, MEAN Stack, MEMR (Mango, Express, MySQL, React) Stack and other Modern We...