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

About this course

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 EncoderChapter - 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 MatrixChapter: 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 IndexChapter: UnSupervised Learning: Association R

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