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

  1. Linear

  2. Polynomial

  3. Regularization

2-3

ML Techniques

Classification

  1. Least Square

  2. Perceptron

  3. Naive Bayes

4-6

Sepal

Petal

ML Techniques

Classification

  1.  KNN

  2. Linear SVM

  3. NonLinear SVM

7-8

Sepal

Petal

ML Techniques

Classification

  1. Decision Tree

  2. Bagging

  3. Boosting

9-10

Sepal

Petal

ML Techniques

Clustering

  1. K-Means

  2. HCA

11

ML Techniques

Classification

  1. Neural Networks

  2. 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

Feel free to ask any questions.

MLT_ MLP_Orientation_14_April_2022

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