Machine Learning Techniques
&
Machine Learning Practices
Orientation
Faculty
Ashish (Vijay) Tendulkar,
Machine Learning Specialist ,
(Google)
Machine learning,
and Deep learning with applications to Natural Language Processing
Instructors
Anamika
Arun
Amrutha
Karthik
Swarnim
Debajyoti
Nitin
Jimmi
ML Techniques
Regression
Classification
Clustering
1
An Introduction
ML Techniques
Regression
-
Linear
-
Polynomial
-
Regularization
2-3
ML Techniques
Classification
-
Least Square
-
Perceptron
-
Naive Bayes
4-6
Sepal
Petal
ML Techniques
Classification
-
KNN
-
Linear SVM
-
NonLinear SVM
7-8
Sepal
Petal
ML Techniques
Classification
-
Decision Tree
-
Bagging
-
Boosting
9-10
Sepal
Petal
ML Techniques
Clustering
-
K-Means
-
HCA
11
ML Techniques
Classification
-
Neural Networks
- Back Propagation
12
Sepal
Petal
Does this course have any programming Components?
def loss(X, w, y):
y_hat = predict(X, w)
samp_loss = np.maximum(-1*y_hat*y, np.zeros(y.shape[0])))
J = np.sum(samp_loss)
return J
How much effort do I need to put into per week?
3 Hours for
watching
Live sessions
Activity Questions
Practice Graded
Machine Learning Practice
MLF
MLT
MLP
Make a hot & sweet drink:
How do you select the required ingredients? How do you mix? Does order of mixing matter? How do you assess the quality? ...
All about ingredients.
Essential and inevitable!
Everything is trivial in cook books
Reading a cook book
Our Kitchen
from sklearn import datasets
from sklearn import pipeline
from sklearn import linear_model
from sklearn import metrics
What is the Grading Policy?
Two Quizzes
Final Exam
Types of Questions:
1. MCQ
2. MSQ
3. NAT
4.Programming
Two Proctored Programming Exam
