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Data Science and Machine Learning Bootcamp

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, and more!

Variables in Python
Data Types in Python
Some complex data types
Working with Sets
Working with Tuples
Working with Dictionaries
Some Practice exercises

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
Scatter plots
Line charts
More about plotting

What is a class?
Object and object instantiation
Class methods
Inheritance in Python Class
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
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

About Mentor
Ashish  Shukla
Arpit Khare

I am pursuing M.Tech CSE from NIT Jamshedpur, a passionate computer science teacher and a coder by hobby. Worked as a Software Development Intern at IIT-Roorkee. My interest fields are: Python, Data Science and Data Structures & Algorithms. I have qualified for GATE and UGC-NET in Computer Science. As a teacher my goal is to inculcate the thinking capabilities in the student by making them explore more about their interested field, also to develop their interest in the fields which are in-demand in the industry right now.

Live Classes English 28 Modules

Data Science and Machine Learning Bootcamp

Rs 14999 23999

Starts From Mon, 28 Mar

Timings: Evening Classes on alternate days

Enroll Now
Once the timer hits zero, pricing will be increased to 2399.00/-

We have designed a
flexible program for you

Missed a class?

No worries, watch the recordings later at your convenience from your Archive.

Have doubts?

Fear not, peer to peer group will help you out any issue, big or small.

Get Certificate!

Receive a linkedIn shareable certificate after the completion of live classes

Timings clash?

Our classes are held in the evening to make sure college schedules do not clash with our classes.

College needs time??

Pause your course and restart a month later with the next batch!

We have designed a flexible program for you

Missed a class?

Watch the recording later, with teaching assistants available to solve your doubts

Jobs & class timings clash?

Our classes are held in the evening to make sure college schedules do not clash with our classes.

Want to revise?

Access assignments/notes lifelong and recordings upto 6 months post course completion

Have Doubts?

Get them resolved over text / video by our expert teaching assistants!

College / family needs time??

Pause your course and restart a month later with the next batch!

We built codekaro for college students
and they love us

Bhanu Pratap Singh Rathore


All the interactive live classes with experienced instructors, the sessions with veteran mentors and the rigorous mock interviews helped bridge the gap in my learning process.

Suryakant Mishra


The mentorship arrangement and the peer culture has helped me evolve as a coder, and I am genuinely grateful for my association with codekaro.

Suman Mahato


I still watch the recorded classes of Codekaro, and try to hone my skills more, codekaro has helped me gain confidence and constantly strengthen my core concepts.

Still have doubts? Request Callback

Still have doubts or query, you can simply request callback and our team will get back to you as soon as possible

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₹12999 ₹14999

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