Control statements
Logical operators
Taking user input
String Formattings

For loops
For loops in dictionary
While loops
While loops with user inputs

Random number library
Generating a random number for the game
Infinite loops
Creating a main menu for the game
Taking input from the user
Comparing the guessed number with the random number
Giving hints to the user
Scoring the user
Terminating the infinite loop

Functions without arguments
Functions with arguments
Functions with returning values
Functions with multiple arguments
Default and Non-default Parameters and Keyword and Non-keyword Arguments
Functions with an Arbitrary Number of Keyword Arguments

The Concept of Processing Files in Python
Reading Text From a File
File Cursor
Closing a File
Opening Files Using "with"
Writing text to a file
Appending Text to an Existing File
Read and Append

Getting Started with pandas
Loading CSV Files
Loading Excel Files
Set Table Header Row
Set Column Names
Set Index Column
Filtering Data from a pandas Dataframe
Deleting Columns and Rows
Updating and Adding new Columns and Rows

What is Numpy?
Indexing Numpy arrays
Slicing Numpy arrays
Iterating numpy arrays
Stacking numpy arrays
Splitting numpy arrays

Syntax Errors
Runtime Errors
Tips on How to become a better programmer

Bar graphs
Pie charts
Histograms
Scatter plots
Line charts
More about plotting

What is a class?
Object and object instantiation
Class methods
Inheritance in Python Class
Encapsulation
Polymorphism
Data abstraction

Basic mathematics behind Machine Learning
Machine learning applications

Supervised Machine Learning
Unsupervised Machine Learning
Difference between Supervised and Unsupervised Machine Learning

Linear Regression Intuition
Libraries required for Linear Regression
Linear Regression with Python

Why do need to clean the data
How we can clean the data
Normalising the data

Learn How to Specify a Data Science Problem with Box Office Revenue Prediction .
Use Matplotlib to visualise and better understand your data.
Learn about the theory of Linear Regression and how it works.
Need for splitting the data into training and testing data
Splitting the data into training and testing data
Creation of the Linear Regression Model
Training the model using the training data
Classification report and accuracy score
Evaluating the model
Estimate and interpret regression coefficients using scikit learn

Need for Multiple Linear Regression
Multiple Linear Regression Intuition
Libraries required for Multiple Linear Regression
Multiple Linear Regression with Python

Idea behind Bayesian Information Criterion
Need for Bayesian Information Criterion

Explore a dataset by examining summary statistics , finding missing values , and discovering outliers.
Predict House Prices using Boston Dataset.
Understand how to work with index and dummy variables in datasets.
Learn how to diagnose and address problems like Multicollinearity in Linear Regression.
Learn how to transform your data to improve your model .
Evaluate your model's performance and learn how to choose among different regression models using the Bayesian Information Criterion.
Learn how to compare the actual and the predicted values of your regression and what you can learn from the residuals.

Visualise your data using Pie charts
Visualise your data using Donut charts
Text Visualisation using Word Clouds.

Introduction to natural language processing to convert, stem, and tokenize text data.
Learn how to install third party packages and dependencies.
Learn how to check for membership in a Python collection.

Intuition behind SVM
Hyperplanes
Marginal Planes
Support vectors
Maximum Margins
Most suitable hyperplane

Idea behind TF-IDF
Need for TF-IDF
Using TF-IDF in Python

Idea behind Pipelines
Advantages of using Pipelines
Using pipelines in Python

Intuition behind Linear SVC
Analysing the dataset
Understanding the problem statement
Training the LinearSVC model for the development of Spam Detector
Detection of the spams using our very own spam detector

Analysing the dataset
Understanding the problem statement
Understanding the types of sentiments
Creation of the model for our project
Training the model with the Product reviews dataset
Evaluating the model using classification report and accuracy score.

Aptitude Skills
Practice Material Quiz
Aptitude Skills Practice Test
Aptitude Skills Practice Test
Mentorship Session by Industry Expert
Resume Building
Applying for jobs

Guaranteed Internship at codekaro