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)