CHAITANYA BHARATHI INSTITUTE OF TECHNOLOGY (A)
E1-E2-E3-Basics of Machine Learning Using Python(VII)-VIII-2020-21
0%
Previous
Course data
General
Announcements
mid and slip test marks
mid-2 question-wise marks
Course exit survey
UNIT-1
Introduction to Python
Data Structures in Python
Introduction to Machine Learning
UNIT-2
DATA VISUALIZATION
Dataframe handling
Feature engineering
UNIT-3
Regression Notes
regression slides
Regularization
Decision Trees
Decision Tress2 using Gini method
Naive Bayes and Random forest
Logistic regression
UNIT-4
K-means Clustering
Hierarchical clustering
UNIT 5
Neural networks PPT
Neural networks word doc
Assignments and Sliptests
SLIP TEST-1
SLIP TEST-1 (reconduct)
ASSIGNMENT-1
SLIP TEST-2
Slip test-3
Online class links
Class-1: Introduction to PYTHON(on 8/2/2021 from 9.10-10.10)
Class-2: IDE Installation(on 09-02-2021 from 9.10-10.10)
Class-3: Variables, Indentifiers and Strings(on 10-02-2021 from 9.10-10.10)
Class-4: Conditional Statements, Loops(on 15-02-2021 from 9.10-10.10)
Class-5: Functions and Arrays(on 16-02-2021 from 9.10-10.10)
Class-6: Lists(on 17-02-2021 from 9.10-10.10)
Class-7: Set, Tuple and Dictionary(on 22-02-2021 from 9.10-10.10)
Class-8: ML PROCESS(on 23-02-2021 from 9.10-10.10)
Class-9: Data visualization (on 24-02-2021 from 9.10-10.10)
Class-10: Data visualization(on 1-03-2021from 9.10-10.10)
Class-11: Data visualization(on 2-03-2021 from 9.10-10.10)
Class-12: Data visualization(on 8-03-2021 from 9.10-10.10)
Class-13: Dataframe Handling(on 9-03-2021 from 9.10-10.10)
Class-14: Dataframe Handling(on 10-03-2021 from 9.10-10.10)
Class-15: Feature selection(on 15-03-2021 from 9.10-10.10)
Class-16: Feature engineering(on 16-03-2021 from 9.10-10.10)
Class-17: Handling categorical and time-based features(on 17-03-2021 from 9.10-10.10)
class 18: Handling text features and missing data(on 22-03-2021from 9.10-10.10)
class 19: Linear regression (on 23-03-2021 from 9.10-10.10)
class 20: Gradient Descent approach(on 24-03-2021 from 9.10-10.10)
class 21: Multiple Linear Regression(on 30-03-2021 from 9.10-10.10)
class 22: Multiple Linear and polynomial Regression(on 31-03-2021 from 9.10-10.10)
class 23: Regularization(Ridge and Lasso)(06-04-2021 from 9.10-10.10)
class 24: Python code for Polynomial Regression and Lasso(on 07-04-2021 from 9.10-10.10)
class 25:Python code for Ridge and explaination of K-NN algorithm(on 12-04-2021 from 9.10-10.10)
class 26:Logistic Regression(on 19-04-2021 from 9.10-10.10)
class 27: Introduction to Decision Trees(on 20-04-2021 from 9.10-10.10)
class 28: Decision Trees(on 26-04-2021 from 9.10-10.10)
class 29: Random Forest, Into to Naïve Bayes Theorem(on 27-04-2021 from 9.10-10.10)
class 30: Naïve Bayes Theorem(on 28-04-2021 from 9.10-10.10)
class 31: Clustering(on 3-05-2021 from 9.10-10.10)
class 32: Divisive Hierarchical Clustering, Text analysis(on 4-05-2021 from 9.10-10.10)
class 33: Text analysis(on 5-05-2021 from 9.10-10.10)
class 34: Text analysis, text classification and time series analysis(on 10-05-2021 from 9.10-10.10)
class 35: time series analysis, window functions and ARIMA (on 11-05-2021 from 9.10-10.10)
class 36: Vectorization, neural networks(on 12-05-2021 from 9.10-10.10)
class 37: NN Architecture, activation functions and optimizers(on 17-05-2021 from 9.10-10.10)
class 38: DL framework, Recommender system(on 18-05-2021 from 9.10-10.10)
class 39: Revision(on 19-05-2021 from 9.10-10.10)
CODE
Unit-1 code
Unit-2 code
unit-3 code
Unit-4 code
Unit-5 code
Next
CHAITANYA BHARATHI INSTITUTE OF TECHNOLOGY (A)
Side panel
Log in
Username
Password
Remember username
Forgot Password?
Log in
E1-E2-E3-Basics of Machine Learning Using Python(VII)-VIII-2020-21
Home
Skip to main content
Course info
Home
Courses
Electronics and Communications Engineering
UG
Academic Year 2020-21
VIII Semester
E1-E2-E3-Basics of Machine Learning Using Python(VII)-VIII-2020-21
Summary
E1-E2-E3-Basics of Machine Learning Using Python(VII)-VIII-2020-21
Teacher:
Smt. E. Swathi Assistant Professor
Skill Level
:
Beginner