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Udemy

Complete Data Science,Machine Learning,DL,NLP Bootcamp

via Udemy

Overview

Master the theory, practice,and math behind Data Science,Machine Learning,Deep Learning,NLP with end to end projects

What you'll 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

Are you looking to master Data Science,Machine Learning (ML), Deep Learning(DL) 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.

Syllabus

  • Getting Started
  • Python Programming Language
  • Python Control Flow
  • Inbuilt Data Structures In Python
  • Functions In Python
  • Function Practice Question
  • Inbuilt Data Structure - Practice Question
  • Importing Creating Modules And Packages
  • File Handling In Python
  • Exception Handling In Python
  • OOPS Concepts With Classes And Objects
  • Advance Python
  • Data Analysis With Python
  • Working With Sqlite3
  • Logging In Python
  • Python Multi Threading and Multi Processing
  • Memory Management With Python
  • Getting Started With Flask Framework
  • Getting Started With Streamlit Web Framework
  • Getting Started With Statistics
  • Introduction To Probability
  • Probability Distribution Function For Data
  • Inferential Statistics
  • Feature Engineering
  • Exploratory Data Analysis and Feature Engineering
  • Introduction To Machine Learning
  • Understanding Complete Linear Regression Indepth Intuition And Practicals
  • Ridge,Lasso And ElasticNet ML ALgorithms
  • Steps By Step Project Implementation With LifeCycle OF ML Project
  • Logistic Regression
  • Support Vector Machines
  • Naive Bayes Theorem
  • K Nearest Neighbour ML Algorithm
  • Decision Tree Classifier And Regressor
  • Random Forest Machine Learning
  • Adaboost Machine Learning Algorithm
  • Gradient Boosting
  • Xgboost Machine Learning Algorithms
  • Unsupervised Machine Learning
  • PCA
  • K Means Clustering Unsupervised ML
  • Hierarichal Clustering
  • DBSCAN Clustering
  • Silhoutte Clustering
  • Anomaly Detection Machine Learning Algorithms
  • Dockers For Beginners
  • GIT For Beginners
  • End To End Machine Learning Project With AWS,Azure Deployment
  • End To End MLOPS Projects With ETL Pipelines- Building Network Security System
  • MLFlow Dagshub and BentoML-Complete ML Project Lifecycle

Taught by

Krish Naik and KRISHAI Technologies Private Limited

Reviews

4.5 rating at Udemy based on 23686 ratings

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