Practical Machine Learning: 5 Project-Based Learning Series

Hello Everyone!

Welcome to our new course series on machine learning! This course is different from a traditional conceptual learning course because it is completely practical, with step-by-step code implementation in Python. It is a project-based learning series, where you will implement 5 different projects that cover different concepts and key features in machine learning.

The projects in this course include Sentiment Analysis, Text Summarizer, Spam Classifier, Language Identification, and AI Chatbot.

With this course, you will be able to:

• Stop going to multiple online forums to search for solutions

• Stop spending weeks building a PoC or project from scratch

• Stop wasting hours trying to understand solutions and code

• Implement projects with detailed explanations

• Get more confident in delivering Data Science and Data Engineering projects

Accelerate your work with ready-made projects in Machine Learning

Features of projects

• End-to-end implementation

• Real industry grade projects by industry experts

• Ready-made solutions to real business problems

• Detailed Explanations

Project details:

1. Sentiment Analysis

2. Text Summarizer

3. Spam Classifier

4. Language Identification

5. AI chatbot

Let’s see brief of each of the projects:

1. Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.

2. Text summarization is a very useful and important part of Natural Language Processing (NLP). First let us talk about what text summarization is. Suppose we have too many lines of text data in any form, such as from articles or magazines or on social media. We have time scarcity so we want only a nutshell report of that text. We can summarize our text in a few lines by removing unimportant text and converting the same text into smaller semantic text form.

3. Spam Classifier: Most of us should be familiar with spam emails. Cisco defines it as unwanted junk email sent out in bulk to an indiscriminate recipient list. Typically, spam is sent for commercial purposes. It can be sent in massive volume by botnets, networks of infected computers. Therefore, spam email filtering is an essential feature for email services such as Outlook and Gmail. Services providers are extensively using Machine learning techniques to filter and classify them successfully.

4. Language Identification: Using the text we have to create a model which will be able to predict the given language. This is a solution for many artificial intelligence applications and computational linguists. These kinds of prediction systems are widely used in electronic devices such as mobiles, laptops, etc for machine translation, and also on robots. It helps in tracking and identifying multilingual documents too. The domain of NLP is still a lively area of researchers.

5. AI chatbot: Were you ever curious as to how to build a talking ChatBot with Python and also have a conversation with your own personal AI? As the topic suggests we are here to help you have a conversation with your AI today. To have a conversation with your AI, you need a few pre-trained tools which can help you build an AI chatbot system. In this series of videos, we will guide you to combine speech recognition processes with an artificial intelligence algorithm.

Use cases of Sentiment Analysis

In this series of project based learning video tutorials, we’re going to teach about Sentiment Analysis. In fact you are going to implement it as a python project. We’re found over the internet there’re tons of course videos or even solutions or source code available but solving alone or trying to understand alone is quite boring or sometimes misinterpretation can hit you hard with time and effort. So if someone already worked on machine learning and has knowledge, he / she can make a video for you so that you don’t face or repeat the same mistake.

So, Proof of concepts with online mentoring sessions would be more worthwhile and take the next step in your career at lowest price. Don’t Struggle to build an NLP project on your own And have fun doing it. We don’t think you should have to figure all things out by yourself. Work with someone who has been in your shoes.

Let’s course overview in brief

As the name suggests Sentiment Analysis, it means to identify the view or emotion behind a situation. It basically means to analyze and find the emotion or intent behind a piece of text or speech or any mode of communication.

User Review 1: I love this cheese sandwich, it’s so delicious.

User Review 2: This chicken burger has a very bad taste.

User Review 3: I ordered this pizza today.

Real-World Example –

There was a time when the social media services like Facebook used to just have two emotions associated with each post, i.e You can like a post or you can leave the post without any reaction and that basically signifies that you didn’t like it. But, over time these reactions to post have changed and grew into more granular sentiments which we see as of now, such as “like”, “love”, “sad”, “angry” etc.

Basic Python Libraries

1. Pandas – library for data analysis and data manipulation

2. Matplotlib – library used for data visualization

3. Seaborn – a library based on matplotlib and it provides a high-level interface for data visualization

4. WordCloud – library to visualize text data

5. re – provides functions to pre-process the strings as per the given regular expression

This course is perfect for anyone looking to build a solid foundation in machine learning and increase their job opportunities. So, don’t wait any longer and enroll now! We hope you enjoy the course. Thank you for your time!

Who this course is for:

• Beginner Machine Learning Developers curious about Natural language processing (NLP)