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Machine Learning Course

for Beginners and Experienced Learners

Learn to use data to make predictions

4.7

(1K+ students)

11K+

students enrolled

10+

problems

6

months

40+

hours of video lectures

Machine Learning

Projects that you will build

Gradient Descent Implementation

Implement the standard Gradient Descent algorithm for optimisation of a model (Regression or Neural)

Logistic Regression Implementation

Implement the standard Logistic Regression model generally used for classifying data into binary classes such as pass/fail, win/lose, alive/dead or healthy/sick.

Decision Tree Implementation

Implement the standard Decision Tree Class used for classifying data into various classes using a tree-like model of decisions and their possible consequences.

Image Caption Generation

Build a CNN/LSTM based model to provide a caption to the given image.

Distracted Driver Detection

Build a classification model to predict using a database of images whether a given driver is distracted, ie, texting, on a call, driving safely etc

Text Generation

Build a Neural Network based model to predict what the next word will be in a sequence of words/sentences

Neural Machine Translation

Build an advanced model for the purpose of translation of phrases and symbols from one language to the other using Artificial Neural Network.

Urban Sound Classification

Build a Neural network based model to classify various sounds using their unique spectrogram into classes such as Dog Barking, Sirens, Street Music etc.

Text Classification

Build a classifier model using Naive Bayes algorithm to predict the topic of an article present in a newspaper

Image Classification (CIFAR-10 Dataset)

Build a classifier for classifying 10,000 images into 10 classes (dog, horse, cat etc) using the CIFAR-10 Dataset.

Twitter Sentiment Analysis

Analyse the tweets posted on twitter to predict the sentiment of the tweet i.e. positive, negative or neutral

Facial Emotion Recognition

Build an advanced model with the ability to predict the facial emotion of a person in an image.

Select a plan based on your needs

Basic

EMI Starts at: $34 per month*

Standard

With Data Structures & Algorithms

EMI Starts at: $48 per month*

Pro

With Mock Interviews & Placement Preparation

EMI Starts at: $65 per month*

Premium

Recommended Best Seller

With Data Structures & Algorithms

With Mock Interviews & Placement Preparation

EMI Starts at: $80 per month*

*EMI calculated for 6 months

12

Months

100+

Hours

9+

Projects

400+

Problems

Bonus Features

Spotlight Hirist Account

Dedicated Placement Cell with curated job openings

Modules Covered

View Curriculum

Introduction to Programming

Data Structures and Algorithms

Machine Learning & Deep Learning

Special Features

1:1 Mock-Interview & Guidance-session with Industry Mentors

Workshops for building your resume and Linkedin/Github profiles

Curated interview problems

100

DSA based product companies Mock Test Series

2 months Free Course Extension

90 Days Course Pause

1:1 Doubt support with Teaching Assistants

Available in English

$686/- or EMI $114/mon*

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$480/- or EMI $80/mon*

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Find more batch dates which suit your schedule View more batches

Course Pause

My exams are coming; I don’t know when my college will start due to COVID; I have lots of work at office, should I take the course or not?

We have introduced a Course Pause feature.

Through this, you can pause and extend your course for a certain number of days based on your course duration and easily continue with your college and semester exams, even if the college reopens in between the course.

For working professionals as well if you have a period where you have lots of work in office you can pause this course and continue once you have a lean period.

The course pause days will be based on the course duration so check your plan for the exact number of pause days

Build Facial Emotion Recognition, Distracted Driver Detection projects & impress recruiters to land great jobs

What our students say about this course?

I have taken data science and machine learning course from coding ninjas and this course has changed my life . By doing this course I easily got an internship as data analyst Thank you coding ninjas team luv ya all

Ankush Sir has extremely well knowledge of the topics and his teaching methods are easy and dedicated too. There is a well-dedicated environment for learning. The Teaching Assistants and other staff are helpful too. I personally recommend taking courses in Coding Ninjas

Ankush Sir was a great help and I had not done much development then. He helped all the students implement the main Machine Learning algorithms from scratch and how to go about using an API and frameworks. Thank you Coding Ninjas for the support and guidance.

Coding Ninjas provided a concrete platform of ML for me and now, on the basis of what I have learned, I am capable of doing really cool projects that are surprisingly appreciated by the industries as well.

Worth of money. This Machine Learning course is pretty much amazing. The video content, assignments, and that video-locked feature are awesome. The slack community of coding ninjas is very helpful.

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Still unsure about the course?

Get a free counselling session from our experts

Call us on our toll free number

1800-123-3598

Best Investment Ever

85% of college students

who complete this course get jobs in tech roles

₹ 10 lpa CTC

Average salary for college students

65x

course fees recovered within first year of new job

95% of working professionals

achieve career transition within 12 months of the program

₹ 24 lpa CTC

Average salary for working professionals

75x

course fees recovered within first year of new job

*considering only package difference because of Coding Ninjas

Your Support System

Meet the team that is invested in your success throughout your journey with us

Faculty

Mentors

Placement Coach

Teaching Assistants

Ankush Singla

Co-Founder & Instructor

He holds a Bachelor’s degree in Computer Science from India’s most premier institute- IIT Delhi and a Master’s degree in Computer Science from Stanford University. He is a coding enthusiast and has worked with bigwigs like Amazon and Facebook in the past.

Exclusive Placement assistance

Get lifetime access to Coding Ninjas placement cell upon clearing the skill tests and become eligible for FREE Premium Account on Hirist with lots of benefits

Become a Coding Ninjas Certified Expert with a Premium Hirist Account

*Only for Pro & Premium

Top students get 6 months premium access to hirist.com, to help with added visibility during the job-search

Curated jobs and internships hand picked for you by Coding Ninjas team

Managed Placements with Coding Ninjas Point of contact to help with the process.

Non-tech interview preparation support and counselling sessions

*Only for Pro & Premium

Team of experts to help you get your resume in shape and to help you tackle softer aspects of the interview process

*Available in select plans only

Luv Misra

Google

Software Engineer

Netaji Subhas University Of Technology

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

Indira Gandhi Delhi Technical University for Women

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

Netaji Subhas University Of Technology

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

Motilal Nehru National Institute of Technology, Allahabad

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

Netaji Subhas University Of Technology

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.

Kartikey Kumar

Coding Ninjas

Teaching Assistant

Dr. A.P.J. Abdul Kalam Technical University, Lucknow

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.

Dharneesh Gupta

HSBC

Software Engineer

TIET - Thapar Institute of Engineering And Technology

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

Jadavpur University

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.

Tools and Technologies Covered

Course Curriculum

Introduction to Programming

Data Structures & Algorithms

Machine Learning

Special Features

Introduction
Introduction to Machine Learning
Introduction to Machine Learning, Supervised Learning, Steps for Supervised learning Loading Boston Dataset, Training an Algorithm
Linear and Logistic Regression
Introduction to Linear Regression
Introduction to Linear Regression, Optimal Coefficients, Cost function, Coefficient of Determination, Analysis of Linear Regression using dummy Data, Linear Regression Intuition
Multivariable Regression and Gradient Descent
Generic Gradient Descent, Learning Rate, Complexity Analysis of Normal Equation Linear Regression, How to find More Complex Boundaries, Variations of Gradient Descent
Project
Gradient Descent
Logistic Regression
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 and Random Forests
Decision Trees - 1
Decision Trees, Decision Trees for Interview call, Building Decision Trees, Getting to Best Decision Tree, Deciding Feature to Split on, Continuous Valued Features
Decision Trees - 2
Code using Sklearn decision tree, information gain, Gain Ratio, Gini Index, Decision Trees & Overfitting, Pruning
Project
Decision Tree Implementation
Random Forests
Introduction to Random Forests, Data Bagging and Feature Selection, Extra Trees, Regression using decision Trees and Random Forest, Random Forest in Sklearn
Naive Bayes
Naive Bayes
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
Project
Text Classification
K-nearest neighbours
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
KNN and SVM
Support Vector Machine
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
Principal Component Analysis
PCA - 1
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 - 2
PCA on Images, PCA on Olevitti Images, Reproducing Images, Eigenfaces, Classification of LFW Images
Project
Cifar10
Natural Language Processing
NLP - 1
Using Words as Features, Basics of word processing, Stemming, Part of Speech, Lemmatization, Building Feature set, Classification using NLTK Naive Bayes
NLP - 2
Using Sklearn classifiers within NLTK, Countvectorizer, Sklearn Classifiers, N-gram, TF-IDF
Project
Twitter Sentiment Analysis
Neural Networks
Neural Networks - 1
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
Neural Networks - 2
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.
TensorFlow and Keras
TensorFlow
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
Keras
Introduction to Keras, Flow of code in Keras, Kera Models, Layers, Compiling the model, Fitting Training Data in Keras, Evaluations & Predictions
Deep Learning Algorithms
CNN - 1
The problem in Handling images, Convolution Neural Networks, Stride and Padding, Channels, Pooling Layer, Data Flow in CNN
CNN - 2
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
RECURRENT NEURAL NETWORK
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
LONG SHORT TERM MEMORY
Vanishing or Exploiting Gradients, Gated Recurrent Units, Variations of the GRU, LSTM
Unsupervised Learning
Unsupervised Learning - 1
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
Unsupervised Learning - 2
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

Frequently Asked Questions

How do I get my doubts resolved?

Which plan should I take? Basic, Standard, Pro or Premium?

Where will the classes be conducted? What are the course timings?

I have done a Coding Ninjas course before. Is there any alumni discount?

How will I benefit from Industry Mentors?

Do I need a computer science background to enrol for this course?

Is there a guarantee that I will get an Internship or job after taking this course?

Which companies hire from Coding Ninjas?

Can I do this program along with my college?

What is the approximate stipend offered in internships through Coding Ninjas placement cell after completing this course?

Can I pay using EMIs?

My exams are coming; I don’t know when my college will start due to COVID; I have lots of work at office, should I take the course or not?

My course is complete and I am unable to access all my content. What should I do?

Still not Convinced? We are champions of awesome free trials.

Machine Learning Course

for Beginners and Experienced Learners

11K+

10+

6

40+

Machine Learning

Projects that you will build

Gradient Descent Implementation

Logistic Regression Implementation

Decision Tree Implementation

Image Caption Generation

Distracted Driver Detection

Text Generation

Neural Machine Translation

Urban Sound Classification

Text Classification

Image Classification (CIFAR-10 Dataset)

Twitter Sentiment Analysis

Facial Emotion Recognition

Select a plan based on your needs

12

100+

9+

400+

Bonus Features

Modules Covered

Special Features

What our students say about this course?

85% of college students

95% of working professionals

Your Support System

Faculty

Mentors

Placement Coach

Teaching Assistants

Ankush Singla

Exclusive Placement assistance

Luv Misra

Aishwarya

Sameer Garg

Mahima Sachan

Anjali Garg

Kartikey Kumar

Dharneesh Gupta

Abir Ajazi

Tools and Technologies Covered

Course Curriculum

Introduction to Programming

Data Structures & Algorithms

Machine Learning

Special Features

Introduction

Linear and Logistic Regression

Decision Trees and Random Forests

Naive Bayes

KNN and SVM

Principal Component Analysis

Natural Language Processing

Neural Networks

TensorFlow and Keras

Deep Learning Algorithms

Unsupervised Learning