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Complete Machine Learning,NLP Bootcamp MLOPS & Deployment

IT & Software

Rating4.7
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Complete Machine Learning,NLP Bootcamp MLOPS & Deployment, available at $44.99, has an average rating of 4.65, with 417 lectures, based on 3724 reviews, and has 25495 subscribers.

You will learn about Master foundational and advanced Machine Learning and NLP concepts. Apply theoretical and practical knowledge to real-world projects using Machine learning,NLP And MLOPS Understand and implement mathematical principles behind ML algorithms. Develop and optimize ML models using industry-standard tools and techniques. Understand The Core intuition of Deep Learning such as optimizers,loss functions,neural networks and cnn This course is ideal for individuals who are Aspiring data scientists and machine learning enthusiasts. or Students and professionals looking to enhance their ML and NLP skills. or Beginners with a basic understanding of programming and mathematics. or Anyone interested in understanding and applying machine learning and NLP techniques from scratch to advanced levels. or Beginners Python Developer who wants to get into the Data Science field It is particularly useful for Aspiring data scientists and machine learning enthusiasts. or Students and professionals looking to enhance their ML and NLP skills. or Beginners with a basic understanding of programming and mathematics. or Anyone interested in understanding and applying machine learning and NLP techniques from scratch to advanced levels. or Beginners Python Developer who wants to get into the Data Science field.

Enroll now: Complete Machine Learning,NLP Bootcamp MLOPS & Deployment

Summary

Title: Complete Machine Learning,NLP Bootcamp MLOPS & Deployment

Price: $44.99

Average Rating: 4.65

Number of Lectures: 417

Number of Published Lectures: 404

Number of Curriculum Items: 417

Number of Published Curriculum Objects: 404

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Master foundational and advanced Machine Learning and NLP concepts.
  • Apply theoretical and practical knowledge to real-world projects using Machine learning,NLP And MLOPS
  • Understand and implement mathematical principles behind ML algorithms.
  • Develop and optimize ML models using industry-standard tools and techniques.
  • Understand The Core intuition of Deep Learning such as optimizers,loss functions,neural networks and cnn

Who Should Attend

  • Aspiring data scientists and machine learning enthusiasts.
  • Students and professionals looking to enhance their ML and NLP skills.
  • Beginners with a basic understanding of programming and mathematics.
  • Anyone interested in understanding and applying machine learning and NLP techniques from scratch to advanced levels.
  • Beginners Python Developer who wants to get into the Data Science field

Target Audiences

  • Aspiring data scientists and machine learning enthusiasts.
  • Students and professionals looking to enhance their ML and NLP skills.
  • Beginners with a basic understanding of programming and mathematics.
  • Anyone interested in understanding and applying machine learning and NLP techniques from scratch to advanced levels.
  • Beginners Python Developer who wants to get into the Data Science field

Are you looking to master Machine Learning (ML) and Natural Language Processing (NLP) from the ground up? This comprehensive course is designed to take you on a journey from understanding the basics to mastering advanced concepts, all while providing practical insights and hands-on experience.

What You’ll Learn:

  • Foundational Concepts: Start with the basics of ML and NLP, including algorithms, models, and techniques used in these fields. Understand the core principles that drive machine learning and natural language processing.

  • Advanced Topics: Dive deeper into advanced topics such as deep learning, reinforcement learning, and transformer models. Learn how to apply these concepts to build more complex and powerful models.

  • Practical Applications: Gain practical experience by working on real-world projects and case studies. Apply your knowledge to solve problems in various domains, including healthcare, finance, and e-commerce.

  • Mathematical Foundations: Develop a strong mathematical foundation by learning the math behind ML and NLP algorithms. Understand concepts such as linear algebra, calculus, and probability theory.

  • Industry-standard Tools: Familiarize yourself with industry-standard tools and libraries used in ML and NLP, including TensorFlow, PyTorch, and scikit-learn. Learn how to use these tools to build and deploy models.

  • Optimization Techniques: Learn how to optimize ML and NLP models for better performance and efficiency. Understand techniques such as hyperparameter tuning, model selection, and model evaluation.

Who Is This Course For:

This course is suitable for anyone interested in learning machine learning and natural language processing, from beginners to advanced learners. Whether you’re a student, a professional looking to upskill, or someone looking to switch careers, this course will provide you with the knowledge and skills you need to succeed in the field of ML and NLP.

Why Take This Course:

By the end of this course, you’ll have a comprehensive understanding of machine learning and natural language processing, from the basics to advanced concepts. You’ll be able to apply your knowledge to build real-world projects, and you’ll have the skills needed to pursue a career in ML and NLP.

Join us on this journey to master Machine Learning and Natural Language Processing. Enroll now and start building your future in AI.

Course Curriculum

Chapter 1: Getting Started

Lecture 1: Welcome To The Course

Lecture 2: Complete Materials

Lecture 3: Anaconda Installation

Lecture 4: Getting Started With VS Code

Chapter 2: Python Programming Language

Lecture 1: Getting Started With VS Code

Lecture 2: Different Ways of Creating Virtual Environment

Lecture 3: Solve Conda Not Recognized Isssue

Lecture 4: Python Basics-Syntax And Semantics

Lecture 5: Variables In Python

Lecture 6: Basics Data Types In Python

Lecture 7: Operators In Python

Lecture 8: Coding Excercise And Assignments

Chapter 3: Python Control Flow

Lecture 1: Conditional Statements (if, elif, else)

Lecture 2: Loops In Python

Lecture 3: Coding Excercise And Assignments

Chapter 4: Inbuilt Data Structures In Python

Lecture 1: List And List Comprehension In Python

Lecture 2: List Practice Code And Assignment

Lecture 3: Tuple In Python

Lecture 4: Tuple Assignment And Practise Code

Lecture 5: Sets In Python

Lecture 6: Sets Assignment and Practise Code

Lecture 7: Dictionaries In Python

Lecture 8: Dictionaries Assignments And Practise Questions

Lecture 9: Real world Usecases Of List

Chapter 5: Functions In Python

Lecture 1: Getting Started With Functions

Lecture 2: More Coding Example With Functions

Lecture 3: Lambda Function In Python

Lecture 4: Map Function In Python

Lecture 5: Filter Function In Python

Lecture 6: Functions Assignments and Practise Content

Chapter 6: Importing Creating Modules And Packages

Lecture 1: Import Modules And Packages In Python

Lecture 2: Standard Library Overview

Lecture 3: Packages Assignment And Practise Questions With Solutions

Chapter 7: File Handling In Python

Lecture 1: File Operation In Python

Lecture 2: Working With File Paths

Lecture 3: File handling Operation Assignment With Solutions

Chapter 8: Exception Handling In Python

Lecture 1: Exception Handling With try except else and finally blocks

Lecture 2: Exception Handling Practise Assignments And Solution

Chapter 9: OOPS Concepts With Classes And Objects

Lecture 1: Classes And Objects In Python

Lecture 2: Classes And Objects Practise Questions And Solutions

Lecture 3: Inheritance In OOPS

Lecture 4: Polymorphism In OOPS

Lecture 5: Encapsulation In OOPS

Lecture 6: Abstraction In OOPS

Lecture 7: Practise Assignments With Solutions

Lecture 8: Magic Methods In Python

Lecture 9: Operator Overloading In Python

Lecture 10: Custom Exception Handling

Lecture 11: Complete OOPS Practise Question With Solutions

Chapter 10: Advance Python

Lecture 1: Iterators In Python

Lecture 2: Generators With Practical Implementation

Lecture 3: Function Copy,Cloures And Decorators

Lecture 4: Advance Python Practise Questions And Solutions

Chapter 11: Data Analysis With Python

Lecture 1: Numpy In Python

Lecture 2: Pandas- DataFrame And Series

Lecture 3: Data Manipulation With Pandas And Numpy

Lecture 4: Reading Data From Various Data Source Using Pandas

Lecture 5: Data Visualization With Matplotlib

Lecture 6: Data Visualization With Seaborn

Lecture 7: Pandas And Numpy Assignments And Solutions

Chapter 12: Working With Sqlite3

Lecture 1: SQLITE3 Assignments And Solutions

Lecture 2: Crud Operation With SQLite3 And Python

Chapter 13: Logging In Python

Lecture 1: Logging Practical Implementation In Python

Lecture 2: Logging With Multiple Loggers

Lecture 3: Logging With A Real World Example

Lecture 4: Logging Assignments And Solutions

Chapter 14: Python Multi Threading and Multi Processing

Lecture 1: What Is Process And Threads

Lecture 2: 2-Multithreading Practical Implementation With Python

Lecture 3: Multiprocessing Practical Implementation With Python

Lecture 4: Thread Pool Executor and Process Pool

Lecture 5: Implement Web Scraping Usecase With Multithreading

Lecture 6: Real World Usecase Implementation With MultiProcessing

Chapter 15: Memory Management With Python

Lecture 1: Memory Allocation And DeallocationGarbage collection and Best Practises

Chapter 16: Getting Started With Flask Framework

Lecture 1: Introduction To Flask Framework

Lecture 2: Understanding Simple Flask App Skeleton

Lecture 3: Integrating HTML With Flask Web App

Lecture 4: Working With HTTP Verbs Get And Post

Lecture 5: Building Dynamic Url ,Variables Rule And Jinja 2 Template Engine

Lecture 6: Working With Rest API's And HTTP Verbs Put And Delete

Chapter 17: Getting Started With Streamlit Web Framework

Lecture 1: Building Web App Using Streamlit

Lecture 2: Example Of ML App With Streamlit Web App

Chapter 18: Getting Started With Statistics

Lecture 1: What is Statistics And its Application

Instructors

Rating Distribution

  • 1 stars: 28 votes
  • 2 stars: 34 votes
  • 3 stars: 187 votes
  • 4 stars: 1170 votes
  • 5 stars: 2305 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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