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Udemy

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

via Udemy

Overview

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.

This course is designed to cover maximum concepts of machine learning a-z. Anyone can opt for this course. No prior understanding of machine learning is required.


Bonus introductions include natural language processing and deep learning.


Below Topics are covered

Chapter - Introduction to Machine Learning

- Machine Learning?

- Types of Machine Learning


Chapter - Setup Environment

- Installing Anaconda, how to use Spyder and Jupiter Notebook

- Installing Libraries


Chapter - Creating Environment on cloud (AWS)

- Creating EC2, connecting to EC2

- Installing libraries, transferring files to EC2 instance, executing python scripts


Chapter - Data Preprocessing

- Null Values

- Correlated Feature check

- Data Molding

- Imputing

- Scaling

- Label Encoder

- On-Hot Encoder


Chapter - Supervised Learning: Regression

- Simple Linear Regression

- Minimizing Cost Function - Ordinary Least Square(OLS), Gradient Descent

- Assumptions of Linear Regression, Dummy Variable

- Multiple Linear Regression

- Regression Model Performance - R-Square

- Polynomial Linear Regression


Chapter - Supervised Learning: Classification

- Logistic Regression

- K-Nearest Neighbours

- Naive Bayes

- Saving and Loading ML Models

- Classification Model Performance - Confusion Matrix


Chapter: UnSupervised Learning: Clustering

- Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method

- Hierarchical Clustering: Agglomerative, Dendogram

- Density Based Clustering: DBSCAN

- Measuring UnSupervised Clusters Performace - Silhouette Index


Chapter: UnSupervised Learning: Association Rule

- Apriori Algorthm

- Association Rule Mining


Chapter: Deploy Machine Learning Model using Flask

- Understanding the flow

- Serverside and Clientside coding, Setup Flask on AWS, sending request and getting response back from flask server


Chapter: Non-Linear Supervised Algorithm: Decision Tree and Support Vector Machines

- Decision Tree Regression

- Decision Tree Classification

- Support Vector Machines(SVM) - Classification

- Kernel SVM, Soft Margin, Kernel Trick


Chapter - Natural Language Processing

Below Text Preprocessing Techniques with python Code

- Tokenization, Stop Words Removal, N-Grams, Stemming, Word Sense Disambiguation

- Count Vectorizer, Tfidf Vectorizer. Hashing Vector

- Case Study - Spam Filter


Chapter - Deep Learning

- Artificial Neural Networks, Hidden Layer, Activation function

- Forward and Backward Propagation

- Implementing Gate in python using perceptron


Chapter: Regularization, Lasso Regression, Ridge Regression

- Overfitting, Underfitting

- Bias, Variance

- Regularization

- L1 & L2 Loss Function

- Lasso and Ridge Regression


Chapter: Dimensionality Reduction

- Feature Selection - Forward and Backward

- Feature Extraction - PCA, LDA


Chapter: Ensemble Methods: Bagging and Boosting

- Bagging - Random Forest (Regression and Classification)

- Boosting - Gradient Boosting (Regression and Classification)



Taught by

Rishi Bansal

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