Machine Learning Course

Enhance your learning curve with our Machine Learning course. Become proficient in ML concepts such as regression, classification, PCA, NLP and other deep learning techniques

4.7

40+

10+

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 mentors 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 mentorsâ€™ 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

Course updated in 2023

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

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

Love from our alumni

Luv Misra

Google

Software Engineer

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

Salesforce

Software Engineer

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.

Abir Ajazi

Deutsche Bank

NA

The main aim here at Coding Ninjas is to inculcate interest in the topics being taught. Each lecture followed a pattern in which, first basics were cleared, and then only advanced topics were initiated. All in all, it's been a great experience.

Kartikey Kumar

Collegedunia

Associate Data Analyst

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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