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All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python]


 All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python]


All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python] Complete hands-on Machine Learning Course with Data Science, NLP, Deep Learning and Artificial Intelligence

What you'll learn

  • Master in creating Machine Learning Models on Python
  • Visualizing various ML Models wherever possible to develop a better understanding about it.
  • How to Analyse the Data, Clean it and Prepare (Data Preprocessing Techniques) it to feed into Machine Learning Models.
  • Learn the most Basic Mathematics behind Simple Linear Regression and its Best fit line.
  • What is Gradient Descent, how it works Internally with full Mathematical explanation.
  • Make predictions using Simple Linear Regression, Multiple Linear Regression.
  • Deploy your own model on AWS using Flask so that anyone can access it and get the prediction.
  • Make predictions using Logistic Regression, K-Nearest Neighbours and Naive Bayes.
  • Fundamental Concept of Deep Learning and Natural Language Processing. Python Code is include at some place for explanation.
  • Regularisation and idea behind it. See it in action using Lasso and Ridge Regression.



यह कोर्स मशीन लर्निंग के अधिकतम कॉन्सेप्ट को कवर करने के लिए बनाया गया है। कोई भी इस कोर्स का विकल्प चुन सकता है। मशीन लर्निंग की कोई पूर्व समझ आवश्यक नहीं है।

बोनस परिचय के रूप में नेचुरल लैंग्वेज प्रोसेसिंग और डीप लर्निंग शामिल है।

Below Topics are covered 

Chapter - Introduction to Machine Learning

  1. - Machine Learning?
  2. - Types of Machine Learning

Chapter - Setup Environment

  1. - Installing Anaconda, how to use Spyder and Jupiter Notebook
  2. - Installing Libraries

Chapter - Creating Environment on cloud (AWS)

  1. - Creating EC2, connecting to EC2
  2. - Installing libraries, transferring files to EC2 instance, executing python scripts

Chapter - Data Preprocessing

  1. - Null Values
  2. - Correlated Feature check
  3. - Data Molding
  4. - Imputing
  5. - Scaling
  6. - Label Encoder
  7. - On-Hot Encoder

Chapter - Supervised Learning: Regression

  1. - Simple Linear Regression
  2. - Minimizing Cost Function - Ordinary Least Square(OLS), Gradient Descent
  3. - Assumptions of Linear Regression, Dummy Variable
  4. - Multiple Linear Regression
  5. - Regression Model Performance - R-Square
  6. - Polynomial Linear Regression

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