Description for course deal

This course is designed to understand basic Concept of Machine Learning.  Anyone can opt for this course. No prior understanding of Machine Learning is required. 

NOTE: Course is still under Development. You will see new topics will get added regularly.

Now question is why this course?

This Course will not only teach you the basics of Machine learning and Simple Linear Regression. It will also cover in depth mathematical explanation of Cost function and use of Gradient Descent for Simple Linear Regression. Understanding these is must for a solid foundation before entering into Machine Learning World. This foundation will help you to understand all other algorithms and mathematics behind it.

As a Bonus Introduction to Deep Learning and Natural Language Processing is included.

Below Topics are covered till now.

Chapter – Introduction to Machine Learning

– Machine Learning?

– Types of Machine Learning

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 – Natural Language Processing: Various Text Preprocessing Techniques with python Code

Chapter – Deep Learning: Artificial Neural Networks, 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

What will you learn in this course

1. Master in creating Machine Learning Models on Python

2. Visualizing various ML Models wherever possible to develop a better understanding about it.

3. How to Analyse the Data, Clean it and Prepare (Data Preprocessing Techniques) it to feed into Machine Learning Models.

4. Learn the most Basic Mathematics behind Simple Linear Regression and its Best fit line.

5. What is Gradient Descent, how it works Internally with full Mathematical explanation.

6. Make predictions using Simple Linear Regression, Multiple Linear Regression.

7. Make predictions using Logistic Regression, K-Nearest Neighbours and Naive Bayes.

8. Fundamental Concept of Deep Learning and Natural Language Processing. Python Code is include at some place for explanation.

Who this course is for

• Anyone who is looking or dont know from where to start Machine Learning, Deep Learning and Natural Language Processing can opt for this course.
• This will provide a good foundation in understanding concept of Machine Learning.