All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python]
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Brief Introduction
Complete hands-on Machine Learning Course with Data Science, NLP, Deep Learning and Artificial IntelligenceDescription
This course is designed to cover maximum concepts of machine learning. 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)
Requirements
- Requirements
- For Machine Learning Concept no prerequisite. Anyone can do this course.
- Prior Understanding of Python is required.
All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python]
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Complete hands-on Machine Learning Course with Data Science, NLP, Deep Learning and Artificial Intelligence This course is designed to cover maximum concepts of machine learning....
