Input and Output Statements in Python | input() & print() Functions in Python | Python Programming

Input and Output Statements in Python | input() & print() Functions in Python | Python I/O Functions

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Input and Output Statements in Python | input() & print() Functions in Python | Python I/O Functions
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Input and Output Statements in Python | input() & print() Functions in Python | Python Programming
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[Ravindra]

Superb explaination sir... Miru explain chesina full course video untey link comments lo share cheyandi sir

[swamyswamy-yukn]

what a explain sir hat's off you sir

[syamragala]

Wonderful explanation sir .. tq so much...

[avaniapps]

Tnqq so much sir
I understood well . Awesome explanation.

[yadllapallinaninani]

Nice explainnation sir meeru explain cheste subject eppatiki marchipolemu sir

[sindhujasekhar]

Sir
If u don't mine
Nenu send Chesey topics anni videos upload chesaraa
Suppose cheyaledu antey
Please upload the videos sir

Introduction to Python: Features of Python, Data types, Operators, Input and output, Control
Statements.
Strings: Creating strings and basic operations on strings, string testing methods. Lists, Dictionaries,
Tuples.

Functions: Defining a function, Calling a function, returning multiple values from a function,
functions are first class objects, formal and actual arguments, positional arguments, recursive
functions.
Exceptions: Errors in a Python program, exceptions, exception handling, types of exceptions, the
except block, the assert statement, user-defined exceptions.

Introduction to NumPy, Pandas, Matplotlib.
Exploratory Data Analysis (EDA), Data Science life cycle, Descriptive Statistics, Basic tools (plots,
graphs and summary statistics) of EDA, Philosophy of EDA. Data Visualization: Scatter plot, bar
chart, histogram, boxplot, heat maps, etc.

Introduction to Pattern Recognition and Machine Learning: Patterns, features, pattern representation,
the curse of dimensionality, dimensionality reduction. Classification—linear and non-linear. Bayesian,
Perceptron, Nearest neighbor classifier, Logistic regression, Naïve-Bayes, decision trees and random
forests; boosting and and hierarchical; k-means clustering.
Regression.
Cost functions, training and testing a classifier. Cross-validation, Class-imbalance – ways of handling,
Confusion matrix, evaluation metrics.

Introduction to Deep Learning: Multilayer perceptron. Backpropagation. Loss functions.
Hyperparameter tuning, Overview of RNN, CNN and LSTM.
Overview of Data Science Models: Applications to text, images, videos, recommender systems, image
classification, Social network graphs.
At the end of this unit, the students will be able to
 Describe RNN, CNN and (L2)
 Explain the applications of Data Science (L2)

[bhadrijagadeeswarareddy]

Sir basic coding midha classes chepandi

[piratesgamingyt]

13:44 Sir, you shoud keep left parentheses before int for y value and close the corresponding parentheses otherwise it's showing error, thank you!

[march]

Tq sir post python videos regularly sir

[AnilKumar-wzzr]

Sir SQL lo joins Medha video cheyandi sir

[gangumallakrishna]

Sir python lo methods Ani pronounce ceyala methods Ani ceyala sir input output functions antunaru list methods antunaru sir plz give me clarity