'Coding has over 700 languages', '67% of programming jobs arenâ€™t in the
technology industry', 'Coding is behind almost everything that is powered
by electricity', 'Knowing how to code is a major requirement for
astronomers', 'The first computer didnâ€™t use any electricity', 'Do you
know there is a coding language named â€œGoâ€œ', 'Computer programming is one
of the fastest-growing careers', 'Fortran (FORmula TRANslation) was the
name of the first programming language', 'The first programmer was the
daughter of a mad poet', 'Many programming languages share the same
structure', 'Coding will soon be as important as reading', 'How many
programmers does it take to change a light bulb? None, thatâ€™s a hardware
problem', 'Why do Java developers wear glasses? Because they canâ€™t C',
'Software and temples are much the same â€” first we build them, then we
pray', 'An engineer will not call it a bug â€” itâ€™s an undocumented
feature', 'In a room full of top software designers, if two agree on the
same thing, thatâ€™s a majority', 'C programmers never die. They are just
cast into void', 'Knock, knock â€¦ Whoâ€™s there? â€¦ *very long pause* â€¦ Java',
'The best thing about a boolean is even if you are wrong, you are only off
by a bit', 'Linux is only free if your time has no value', 'The computer
was born to solve problems that did not exist before', 'Coding has over
700 languages', '67% of programming jobs arenâ€™t in the technology
industry', 'Coding is behind almost everything that is powered by
electricity', 'Knowing how to code is a major requirement for
astronomers', 'The first computer didnâ€™t use any electricity', 'Do you
know there is a coding language named â€œGoâ€œ', 'Computer programming is one
of the fastest-growing careers', 'Fortran (FORmula TRANslation) was the
name of the first programming language', 'The first programmer was the
daughter of a mad poet', 'Many programming languages share the same
structure', 'Coding will soon be as important as reading', 'How many
programmers does it take to change a light bulb? None, thatâ€™s a hardware
problem', 'Why do Java developers wear glasses? Because they canâ€™t C',
'Software and temples are much the same â€” first we build them, then we
pray', 'An engineer will not call it a bug â€” itâ€™s an undocumented
feature', 'In a room full of top software designers, if two agree on the
same thing, thatâ€™s a majority', 'C programmers never die. They are just
cast into void', 'Knock, knock â€¦ Whoâ€™s there? â€¦ *very long pause* â€¦ Java',
'The best thing about a boolean is even if you are wrong, you are only off
by a bit', 'Linux is only free if your time has no value', 'The computer
was born to solve problems that did not exist before',

Learn how to source, manipulate and visualise data using Python and its libraries. Build and refine your Machine Learning skills with the help of topics like Statistics, Trees, Neural Networks etc.

4.7

70+

12

Our students that took the course got hired atâ€¦

This is where you embark on an amazing journey!

Clear your doubts with ease

1:1 sessions over voice call & chat with our teaching assistants

Features that keep you going

A structured curriculum that makes learning easy

Weekly milestones to keep you motivated

Practice code problems of varying difficulty

Compile & run in an integrated coding environment

Industry leading experts to help you grow

1:1 Mock interviews with resume and career guidance

Structured feedback to make you better

Get a chance to be referred to your expertsâ€™ company

Most flexible program in the industry

Freedom to learn

Cheat days

Comprehensive placement package to make you job ready

Get access to an expert placement coach,

professional resume & portfolio services, and Hirist

spotlight benefits, with close focus on soft skills

professional resume & portfolio services, and Hirist

spotlight benefits, with close focus on soft skills

The results

11 LPA

40x

95%

"I would like to dedicate my coding journey to Coding Ninjas. I found their courses very helpful in developing my basic programming concepts."

Sudhanshu Kumar

"The course pause feature is a boon to students like me who are pretty irregular with schedules. The course structure helped me learn everything step by step."

Nishant Birla

Choose the plan that works for you

Data Science

Introduction to Programming

Data Structures and Algorithms

Data Science & Machine Learning

$201

$340

1:1 Expert Doubt support

90 Days course pause

1:1 sessions with Industry Mentors

Resume and profile Building Workshops

2 months Free Course Extension

Curated Interview Problems

Mock Test Series for Product Companies

Data Science + Data Structures

Introduction to Programming

Data Structures and Algorithms

Data Science & Machine Learning

$288

$485

1:1 Expert Doubt support

120 Days course pause

1:1 sessions with Industry Mentors

Resume and profile Building Workshops

2 months Free Course Extension

Curated Interview Problems

Mock Test Series for Product Companies

Data Science + Placement Preparation

Introduction to Programming

Data Structures and Algorithms

Data Science & Machine Learning

$364

$615

1:1 Expert Doubt support

90 Days course pause

1:1 sessions with Industry Mentors - 10

Spotlight HIRIST to Elevate your career

2 months Free Course Extension

Curated Interview Problems - 100

Mock Test Series for Product Companies

Data Science + Data Structures + Placement Preparation

Introduction to Programming

Data Structures and Algorithms

Data Science & Machine Learning

$412

$695

1:1 Expert Doubt support

120 Days course pause

1:1 sessions with Industry Mentors - 10

Spotlight HIRIST to Elevate your career

2 months Free Course Extension

Curated Interview Problems - 100

Mock Test Series for Product Companies

Course curriculum for the curious

Introduction to flowcharts, Decision making using flowcharts, Loops, Example problems

First program, Variables and data types, Taking input, How data is stored in memory, Arithmetic Operators

Introduction to If else, Relational and logical operators, Nested conditionals

While loops, Flow of execution of statements in while loop, Example problems using while loop

Introduction to patterns, Basic Patterns, Square Patterns, Triangular Patterns, Character Patterns, Reverse Triangle, Inverted patterns, Isosceles triangles

For loops, Break and Continue, increment - decrement operators

Introduction to functions, Working of function calling, Variables and its scope, Pass by value

Introduction to arrays/lists, How arrays/lists are stored in memory, Passing arrays/lists to functions

Understanding Binary Search, Selection sort, Bubble sort, Insertion sort, Merging two sorted arrays

Introduction to strings, storage of strings and theirinbuilt functions

2D lists, Storage of 2D lists, Example problems using 2D lists

Introduction to recursion, Principle of mathematical induction, Fibonacci numbers, Recursion using arrays, Recursion using strings, Recursion using 2D arrays

Order complexity analysis, Theoretical complexity analysis, Time complexity analysis of searching and recursive algorithms, Theoretical space complexity, Space complexity analysis of merge sort

Introduction to backtracking, Problems based on backtracking: Rat in the maze, Word search, and N-Queens.

Introduction to OOPS, Creating objects, Getters and setters, Constructors and related concepts, Inbuilt constructor and destructor, Example classes

Static members, Function overloading and related concepts, Abstraction, Encapsulation, Inheritance, Polymorphism, Virtual functions, Abstract classes, Exception handling

Introduction to linked list, Inserting node in linked list, Deleting node from linked list, Midpoint of linked list, Merge two sorted linked lists, merge sort of a linked list, Reversing a linked list

Introduction to stacks, Stack using arrays, Dynamic Stack class,Stack using linked list, Inbuilt stack, Queue using arrays, Dynamic queue class, Queue using linked list, Inbuilt queue

Introduction to Trees, Making a tree node class, Taking a tree as input and printing, Tree traversals, Destructor for tree node class

Introduction to Binary Trees, Taking a binary tree as input and printing, Binary Tree traversals, Diameter of binary tree

Introduction to Binary Search Trees, Searching a node in BST, BST class, Inserting and Deleting nodes in BST, Types of balanced BSTs

Introduction to Priority Queues, Ways to implement priority queues, Introduction to heaps, Introduction to Complete Binary Trees and its implementation, Insert and Delete operations in heaps, Implementing priority queues, Heap sort, Inbuilt Priority Queue

Introduction to Hashmaps, Inbuilt Hashmap, Hash functions, Collision handling, Insert and Delete operation implementation in hashmap, Load factor, Rehashing

Introduction to Tries, Making a Trie Node class, Insert, Search and Remove operation implementation in Tries, Types of Tries, Huffman Coding

Introduction to Graphs, Graph Terminology, Graph implementation, Graph Traversals (DFS and BFS), Weighted and Directed Graphs, Minimum Spanning Trees, Cycle Detection in Graphs, Kruskal's algorithm, Prim's Algorithm, Dijkstra's algorithm

ntroduction to Memoization, Introduction to Dynamic Programming, Fibonacci numbers using recursion, memoization and dynamic programming

Longest Common Subsequence (LCS) using recursion, memoization and dynamic programming, Edit distance using recursion, memoization and dynamic programming, Knapsack problem using recursion, memoization and dynamic programming

What is Data Science? Work of Data Scientist, Data Science and ML, Why Python

First Program in Python, Anaconda and Jupyter Notebook, Variables in Python, Data Types, Python Numbers, Limit of Integers, Arithmetic Operators, Taking Inputs

Boolean Datatype, Introduction to If-Else, Using Relational and Logical Operators, Using Else If, Nested Conditionals, While Loop, Primality Checking, Nested Loops

Introduction to Patterns, First Patterns, Square Patterns, Triangular Patterns, Character Patterns, Inverted Pattern, Reversed Pattern, Isosceles Pattern

For loop & Range Method, Print Multiples of 3, Check if a Number is Prime, Pattern, Break Keyword, Else keyword with loops, Continue keyword, Pass statements

Functions and how to use them, Why do we need functions, How does function calling works, Functions using strings & lists, Swap Alternate, Scope of Variables, Default parameters in functions

Introduction, Create class & object, Instance Attributes, Class Attributes, Methods, Instance Methods, Constructors, Access modifiers, Class Methods & Static Methods

Strings Introduction, Strings inbuilt functions, Strings slicing, Lists Introduction, List inbuilt functions, Taking Input, Difference of Even-Odd, List Slicing, Multi-dimensional Lists

Tuples, Tuples Functions, Variable-length input and output, Dictionary Intro, Access/looping elements in dictionary, Adding Or Removing Data In Dictionary, Print All Words With Frequency K, Sets Intro, Functions in sets, Sum Of All Unique Numbers In List

Introduction, Open and read Text files, Read file line by line, CSV Files, Work with CSV Files, DictReader, Countrywise Killed

Introduction, Why NumPy is fast, Create NumPy arrays, Slicing & Indexing, Mathematical Operations - 1D, Boolean Indexing - 1D, Boolean Indexing - 2D, NumPy Broadcasting

Introduction to Pandas, Accessing Data in Pandas, Manipulating Data in Data Frame, Handling NAN, Handling Strings in Data

Plotting Graphs, Customizing Graph, Bubble Chart, Pie Chart, Histogram, Bar Graph, How to decide Graph Type

Create and Insert, Update Table, Retrieve Data, Filter Result, Aggregate Functions, Update and Delete, Introduction to Databases, Relational Database, What is SQL

Group By, Having, Order By, IN, BETWEEN, LIKE, Joins Introduction, Inner Join, Left & Right Join

What is Indexing, Default Indexing, Use Default Indexing, Add & Remove Indexes, SQLite Introduction, Connect with a database, Passing parameters in a query, Fetch data, SQLite with pandas

Introduction to APIs, Examples of APIs, HTTP Basics, HTTP Libraries, JSON file format, JSON to Python, Explore JSON data, Passing Parameters - 1, POST request

Basic Authentication, Reddit Introduction, oAuth Introduction, oAuth Roles & Process, Reddit API - Get Access Token, Reddit API - Fetch Data, Reddit API - Few more operations

Scraping Introduction, HTML tour, BeautifulSoup Introduction, Navigating Parse Tree, First Web Page, Books to scrape, Link of all the pages, Store data in CSV

Selenium Introduction, Letâ€™s start with Selenium, Browser Interaction, Locate element - 1, Web element Methods & Properties, Find all jobs, Type into fields

Implicit Wait, Explicit Wait, Radio buttons and checkbox, Handle dropdown,Infinitely Scroll Webpage, Infinite Scrolling, Switch tab focus, Handle popups

Different ways for Data Visualization, Types Of Data Visualization, What is Data Visualization?, Importance Of Data Visualization

Automatically Generated Fields, Dimension & measure, Tableau Navigation, Data Joins and Union, Connect with Data, Tableau Installation, What is Tableau, Data Types

Histogram, Bar Chart, Area Chart, Adding customization, Letâ€™s create the First plot, Understanding the Basics of Plotting, Types of charts, Line Chart

Categorical Distribution Plots, Categorical Scatter plots, Plotting with Categorical Data, Visualizing Statistical Relationships - ScatterPlot, Seaborn vs Matplotlib, Introduction to Seaborn, Starting with Seaborn, Visualizing Statistical Relationships - LinePlot

Introduction of Statistics, Data Types in Statistics, Sample & Population, Simple Random Sampling, Stratified sampling, Cluster sampling, Systematic Sampling, Categories of Statistics

Measures in Descriptive Statistics, Measures of central tendency, Measures of Spread, Range & IQR, Variance & Standard Deviation, Measure of Position

Introduction to Inferential Statistics, Why Inferential Statistics?, Probability Distribution, Normal Distribution, Standard Normal Distribution, Sampling Distribution, Central Limit Theorem

What is Hypothesis Testing, Null & Alternative Hypothesis, Significance Level, Test statistic, Test Statistic: Critical value & Rejection Region, Test Statistic: Type of Test, Errors in Hypothesis Testing

Introduction to Machine Learning, Supervised Learning, Steps for Supervised learning Loading Boston Dataset, Training an Algorithm

Introduction to Linear Regression, Optimal Coefficients, Cost function, Coefficient of Determination, Analysis of Linear Regression using dummy Data, Linear Regression Intuition

Generic Gradient Descent, Learning Rate, Complexity Analysis of Normal Equation Linear Regression, How to find More Complex Boundaries, Variations of Gradient Descent

GRADIENT DESCENT

Handling Classification Problems, Logistic Regression, Cost Function, Finding Optimal Values, Solving Derivatives, Multiclass Logistic Regression, Finding Complex Boundaries and Regularization, Using Logistic Regression from Sklearn

Decision Trees, Decision Trees for Interview call, Building Decision Trees, Getting to Best Decision Tree, Deciding Feature to Split on, Continuous Valued Features

Code using Sklearn decision tree, information gain, Gain Ratio, Gini Index, Decision Trees & Overfitting, Pruning

DECISION TREE IMPLEMENTATION

Introduction to Random Forests, Data Bagging and Feature Selection, Extra Trees, Regression using decision Trees and Random Forest, Random Forest in Sklearn

Bayes Theorem, Independence Assumption in Naive Bayes, Probability estimation for Discrete Values Features, How to handle zero probabilities, Implementation of Naive Bayes, Finding the probability for continuous valued features, Text Classification using Naive Bayes

TEXT CLASSIFICATION

Introduction to KNN, Feature scaling before KNN, KNN in Sklearn, Cross Validation, Finding Optimal K, Implement KNN, Curse of Dimensionality, Handling Categorical Data, Pros & Cons of KNN

Intuition behind SVM, SVM Cost Function, Decision Boundary & the C parameter, using SVM from Sklearn, Finding Non Linear Decision Boundary, Choosing Landmark Points, Similarity Functions, How to move to new dimensions, Multi-class Classification, Using Sklearn SVM on Iris, Choosing Parameters using Grid Search, Using Support Vectors to Regression

Intuition behind PCA, Applying PCA to 2D data, Applying PCA on 3D data, Math behind PCA, Finding Optimal Number of Features, Magic behind PCA

PCA on Images, PCA on Olevitti Images, Reproducing Images, Eigenfaces, Classification of LFW Images

CIFAR 10

Using Words as Features, Basics of word processing, Stemming, Part of Speech, Lemmatization, Building Feature set, Classification using NLTK Naive Bayes

Using Sklearn classifiers within NLTK, Countvectorizer, Sklearn Classifiers, N-gram, TF-IDF

TWITTER SENTIMENT ANALYSIS

Why do we need Neural Networks, Example with Linear Decision Boundary, Finding Non-Linear Decision Boundary, Neural Network Terminology, No of Parameters in Neural Network, Forward and Backward Propagation, Cost Function, How to handle Multiclass classification, MLP classifier in sklearn

Forward Propagation, Error Function in Gradient descent, Derivative of Sigmoid Function, Math behind Backpropagation, Implementing a simple Neural Network, Optimising the code using Vector Operations, Implementing a general Neural Network.

Introduction to TensorFlow, Constants, Session, Variables, Placeholder, MNIST Data, Initialising Weights and Biases, Forward Propagation, Cost Function, Running the Optimiser, How does the Optimiser work?, Running Multiple Iterations, Batch Gradient Descent

Introduction to Keras, Flow of code in Keras, Kera Models, Layers, Compiling the model, Fitting Training Data in Keras, Evaluations & Predictions

The problem in Handling images, Convolution Neural Networks, Stride and Padding, Channels, Pooling Layer, Data Flow in CNN

The architecture of CNN, Initializing weights, Forward Propagation in TensorFlow, Convolution and Maxpool Functions, Regularization using Dropout layer, Adding Dropout Layer to the network, Building CNN Keras

Building ML Models for sequential Data, Recurrent Neural Networks, How does RNN work, Typical RNN Structures, Airline Data Analysis, Preparing Data for RNN, Setting up the RNN model, Analysing the Output

Vanishing or Exploiting Gradients, Gated Recurrent Units, Variations of the GRU, LSTM

Introduction to Unsupervised Learning, Introduction to Clustering, Using K-means for Flat Clustering, KMeans Algorithm, Using KMeans from Sklearn, Implementing Fit & Predict Functions, Implementing K-Means Class

How to choose Optimal K, Silhouette algorithm to choose K, Introduction to K Medoids, K Medoids Algorithm, Introduction to Hierarchical Clustering, Top down/Divisive Approach, Bottom up/Divisive Approach

Meet the faculty legends that will make you legendary

Ankush Singla

Co-Founder & Instructor

Nidhi Agarwal

Instructor & Founding Member

Love from our alumni

Luv Misra

Google

Software Developer

It's a great place to learn how to code. The way of teaching and dedication offered towards your development makes it easier to grasp the concepts even for beginners.
The best part of Coding Ninjas is the faculty, I am grateful for all the guidance.

Aishwarya

Adobe

Software Engineer

One of the best mentors to guide you are here at Coding Ninjas. Loved the faculty and content, I would ask everyone who wants to learn to program to take up the courses here. Apart from this, practice is the key to ace the skills.

Sameer Garg

Samsung

Software Engineer

The experience of learning at CN was overwhelming. It shaped my mindset towards solving programming questions in a systematic way that still helps me in all coding scenarios. This approach helped me in the placement season a lot.

Mahima Sachan

Microsoft

Software Developer

A platform having perfectly structured courses to build your programming skills. Mentors are highly skilled and I would recommend any aspiring coder to take up these courses. Grateful for all the learnings here at Coding Ninjas

Anjali Garg

Google

Software Engineer

Coding Ninjas is a great platform to start your journey with coding. I joined them in 2018 and completed the C++ course under Nidhi maam's guidance. The course helped me to understand concepts of DS and algo in-depth and moreover helped me to crack many coding tests and interviews. Whether it's faculty, placement cell, TA support, etc, Coding Ninjas is just the best. In the end, I'd like to thank all my mentors at Coding Ninjas for guiding me throughout.

Dharneesh Gupta

HSBC

Software Engineer

It was a great learning experience. The kind of content it provides really helps in building your logic and how to approach a problem in real life too.
Ankush sir has done a wonderful job in explaining the core concept of hard topics.

Kartikey Kumar

aristos erevna consulting pvt. ltd

Project Analysts

The course structure was designed very effectively for both beginners and experienced coders. Support of Mentors and Teaching Assistants helped a lot to improve my coding fundamentals and helping other students enhanced my coding skills.

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