DSA in C++

Master Data science, Data analytics and Machine learning using Python

Learn to play with data using techniques like data gathering, manipulation, cleaning and draw actionable insights post processing in Data science. In addition, master regression, supervised clustering and become an expert ML engineer. You need to be thorough with Python and Mathematics for this course.
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DS,ML,RIL covered

12 Live projects

4/6 months Duration

Classroom | Live | Online Mode of Delivery

Why should you do this course?

Enquire at - 9999579111
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Starting from ₹ 2100/-

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Key Highlights

Deep Learning, Machine Learning, Reinforced Learning modules

12 Live Projects

4/6 Months Duration

Certificate of Excellence/Completion

Placement assistance

Syllabus
  • Data Science Quickstart Mode
    The section gives you a headstart with libraries and modules that are essential to any Data Science Pipeline. This quickstart section helps you to get going as fast as possible with the course.
  • Basics
    Python is the language that we use for scripting and learning Data Science algorithms throughout the course. This section provides a introduction to the basics of Python programming language.
  • Functions
    Functions are the programming paradigm that increases code reusability in our scripts. This section introduces functions in Python along with other high level concepts like Generators and Decorators.
  • Builtin Data Structures
    Data Structures are the skeleton of computer programming. In this section, we look into the inbuilt data structures that python provides us.
  • Object Oriented Programming Models
    Writing good code involves greatly designed classes. We define classes and modules in this section and equip us with important knowledge of OOPs with python.
  • File and Error Handeling
    In this section, we look into file handling with python. We also learn how to safely handle errors in our python programs.
  • Iteartion Protocol and Generator
    In this section, we look back to functions and learn Generators in detail. We see the iteration protocol in python and implement different iterators in python.
  • Asynchronous Programming in Python
    Asynchronous programming is an essential part in production level python scripts. In this section, we look into Async Programming in python.
  • Basics of Git/GitHub
    Version Control Systems are very important in any project development cycle. We look into Git and GitHub in this section.
  • Data Acquisition Web Scraping
    Data Acquisition is the very first stage in any Data Science project. In this section, we see how to acquire data from unstructured data sources from the web.
  • Data Acquisition using Web API
    APIs are an essential part of web applications. In this section, we see how to leverage the power of Web APIs and extract/gather data from these sources.
  • Data Acquisition Web Crawler using Scrapy
    Have you heard about Spiders? No not the insect spiders but web spiders. In this section, we learn about these spiders and crawlers.
  • Web Automation
    Selenium is one of the most used Web Automation tool in the industry. In this section we learn, how to use selenium's python package and automate web browsing.
  • Getting Started with Machine Learning
    This section gives the first introduction to Machine Learning. We discuss different types of learning paradigms and formulate a introductory definition to Machine Learning.
  • Numpy
    This section gives the first introduction to Machine Learning. We discuss different types of learning paradigms and formulate a introductory definition to Machine Learning.
  • Linear Algebra
    Data Science is a culmination of many different fields. This section introduces one of the most important prerequisite for learning Machine Learning, i.e Linear Algebra
  • Two Dimensional Dynamic Programming
  • Data Visualisation
    In this section, we look into some Data Visualisation packages we have in python.
  • Pandas
    The most famous python package of all, the PANDAS. In this section, we will cover the basics of Pandas package.
  • Probability Distribution & Statistics
    Before getting into major topics, this section gives us a gist of Probability and Statistics required to cover more advance in-depth topics.
  • KNearest Neighbours
    In this section, we look into our very first Machine Learning algorithm, K-Nearest Neighbour or KNN
  • Linear Regression
    Linear Regression is one of the widely used algorithm in data analytics and data science firms. In this section we learn Linear Regression and its in-depth analogies.
  • Linear Regression II multiple features
    Linear Regression is one of the widely used algorithm in data analytics and data science firms. In this section we learn Linear Regression and its in-depth analogies.
  • ScikitLearn Introduction
    One of the most import package for Data Science professional, scikit-learn. In this section we learn scikit-learn and see how we can quickly prototype machine learning algorithms in 1-2 lines of python code.
  • Optimisation Algorithms
    This section introduces us to Optimisation methods. We cover the general iterative optimisation methods and its variant
  • Locally Weighted Regression (LOWESS)
    In this section, we cover a non-linear regression estimate, the Locally Weighted Regression technique
  • Maximum Likelihood Estimation (MLE)
    Machine learning is about parameter estimation. In this section we learn one such parameter estimation method, the Maximum Likelihood Estimation.
  • Logistic Regression
    How about a Linear model for classification tasks? This section, we learn Logistric Regression and see how classification is performed in Machine Learning.
  • Data Preprocessing and Feature Selection
    Data preprocessing and Feature selection is the most important and integral part of Exploratory Data Analysis
  • Pricipal Componenet Analysys
    Curse of Dimensionality? This section we learn something to solve the high dimensionality problem. The Principal Component Analysis.
  • Natural Data Processing & Naive Bayes classifier
    Want to work with textual data? We need Natural Language Processing.
  • Decision Tree & Random Forests
    In this section, we see another non-parametric model and learn what ensemble models are.
  • Support Vector Machines
    We have learned so many Linear Models, how about modelling non-linear functions and decision boundaries? In this section we learn Support Vector Machines, the greatest of all before the wave of Deep Learning.
  • Clustering Fundamentals
    In this section we learn about unsupervised learning and introduce KMeans clustering
  • Deep Learning Introduction
    Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. In this section, we dig deep into Deep Learning, we cover major deep learning architectures like Deep Neural Network, Convolutional Neural Network and Recurrent Neural Network and learn Backpropagation & Backpropagation-through-time optimization algorithms.
  • Neural Networks MLP's
  • Convolutional Neural Network
  • Traning Data Loaders, Augumentation, Colab
  • Digging Deeper into Convnets
  • Transfer Learning
  • Markov Chains for Text Generation
  • Recurrent Neural Networks
  • Word Embeddings Word2Vec
  • Reinforcement Learning
    Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. We end our journey with ML by introducing some reinforcement learning algorithms and seeing how it is performed in the industry.
  • Generative Adversarial Networks
    What curriculum teachs machine learning without Generative Modelling. In this section we do an introduction to Generative Models and cover some paradigms like Adversarial Training and Variational Inference.
  • Deep Convolutional GAN's
  • Pytorch
    PyTorch is an open source machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab.
Projects
PokeMon Classification

Learn to classify Pokemons using Transfer Learning.

Web Crawler

Learn to build Spiders using Scrapy for Amazon, PepperFry etc.

Emoji Prediction

Learn to predict emoji's using Recurrent Neural Networks.

Odd One Out

Find out the odd word using Word Embeddings.

Word Analogies

Learn to implement Word2Vec and learn interesting analogies in an unsupervised manner.

Titanic Survivor Prediction

Solve Kaggle like challenge using Decision Trees and Random Forests.

Cartpole Player

Use Reinforcement Learning to teach AI play Cartpole Game.

Dominant Color Extraction

Repaint an image using K-Means Clustering.

Face Recognition

Learn to build a Face Recognition System using OpenCV and Machine Learning.

Diabetes Classification

Use Logistic Regression to classify diabetes patients.

Handwritten Digit Recognition

Use Neural Networks to classify hand-written digits.

Language Generator

Use Markov Chains to make a cool text generator.

Choose Batch

Classroom Batch

4-6 months duration, covers Python, Machine learning, Data analytics with 12 live projects
Priority Placement assistance & Doubt support
Personal mentoring, progress tracking and feedback
Certificate of Excellence/Completion
Free Wildcraft bag, Swags and access to Online course
Developer CV and In-class Hackathons

Courses

Live Batch

70+ live classes, 6-7 months duration, covers Python, Machine learning, Data analytics with 12 live projects
Priority Placement assistance & Doubt support
Personal mentoring, progress tracking and feedback
Certificate of Excellence/Completion
Free Wildcraft bag, Swags and access to Online course
Developer CV and In-class Hackathons

Courses

Online Batch

Highly Economical model to learn
85+ hours Exhaustive content and Tech enabled Guided learning
6 months duration, covers Python, Machine learning, Data analytics with 12 live projects
Live interactive booster classes of difficult topics
Certificate of Excellence/Completion
Progress tracking and feedback
Optional Priority Placement assistance & Doubt support

Courses

Why choose Coding Blocks

Learn and grow as a developer with our project based courses.

Industry-focused curriculum

Superb mentors

Best in class mentors from top Tech schools and Industry favourite Techies are here to teach you.

Career-focused pedagogy

Industry-vetted curriculum

Best in class content, aligned to the Tech industry is delivered to you to ensure you are a darling of the Tech industry.

Best in class mentor

Project based learning

Hands on learning pedagogy with live projects to cover practical knowledge over theoretical one.

Placements

Superb placements

Result oriented courses with placement across all genres, students as well as Working professionals.

Inspirational Success stories of CB Alumni

where hard work and determination meets victory!

Top placements from these programs

Our students can be found in

45x - 200x

Return on Investment

After the completion of the course you will get a ROI of 45 - 200x

Choose Batch

Placement assistance

A dedicated Placement team along with Hiring Blocks, the placement portal of Coding Blocks work round the clock to ensure the best of opportunities in the Tech arena are available to you.

Developer CV and profile Preperation

Developer CV and Dev profile Preperation

Interview Preperation

Interview Preperation

Referrals for Placements and internships

150+ Partner companies for Placements and internships

Mock interviews

Mock interviews

Learning cycle

What an inspiration to all!

Best in class mentors

Coding Blocks has some of the best mentors in the Industry who will remain by your side during your Preperation for teaching, guidance and assistance.

Shubham SinghalInstructor and Product engineer

Manu S PillaiSenior Instructor and Product engineer

Your Code Buddies!

A wide network of TAs aka Teaching assistants, who are typical ex-students of Coding Blocks helps in Doubt resolution along with Mentors, through Video, Audio, Screen share and other media to ensure all your queries are addressed timely

Industry Coaches

Industry experts are guides currently working in Top companies like Google, Amazon, Microsoft and the like and they helps students with invaluable tips on the Industry, Hiring process, Mock interviews and other necessities required for paving the way into the Top Tech companies around the globe.

Tech Established Learning!

The e-learning portal is an in-house developed, state of the art application which uses the best of technology and resources to ensure all learners gain the maximum from their program. It provides immersive learning with suggestions and guidance to ensure even self learning is effective and fruitful. This portal allows learning through ebooks, videos, notes while allowing learners to attempt coding problems, MCQ assignments with attending live classes and asking doubts through chat and live video calling feature with mentors and TAs.

See what students have to say

Frequently Asked Questions

Learn and grow as a developer with our project based courses.

  • Coding Blocks has physical centers in Delhi and Noida. Classroom program batches are conducted in these physical centers where you learn in a class with your peers and mentors, much like the way you do in College or University with a difference that you get to have personal attention by the mentor. Batch size is extremely limited and Course completion is complete for all learners. This is the most successful program in terms of Success or placements.

  • Live interactive program is a perfect replacement to the Classroom program. In a live interactive course, classes are conducted in a controlled environment, where the mentor teaches and students can interact with the mentor, much like a classroom. The only difference is that the mentor is not physically present in front of you. This model allows you to learn from the mentor of your choice, transcending boundaries and is economical as compared to the Classroom program.

  • Self paced Online programs are guided learning courses driven by our state of the art e-learning portal. These programs carry the same legacy as our Classroom programs. In order to reinforce learning and assist students, we have made our Self paced Online programs “HYBRID”, meaning we have added Live classes of topics where we feel students need interaction with mentors. In addition, students can clear their doubts through Video calls or Chats with our mentors and TAs. If learners stay close to our centers, they can even visit our Physical centers and meet the mentors in person to resolve their doubts.

  • If you are facing trouble with any model and want to make a switch, please get in touch with the Coding Blocks Support team for assistance. If need be, we will help you transition from one model to another.

  • The three models of learning are very different in nature and suits different learners basis individual preferences. We would recommend you to connect to our counsellors who can guide you well on which program and model best suits your needs.

  • Doubts are addressed by your Mentors and Teaching assistants who are always there to help you in your program. Doubts can be addressed in person, over live video calls, live chats and screen sharing sessions, pair coding sessions, Slack channels and Whatsapp groups, in addition to our portal where your doubts are also addressed. Don’t worry, we got your back.

  • Once you complete your course, you need to apply for the position of TA in Coding Blocks. Your application will be screened, you will be interviewed and tested on Coding problems and then you will be roped in. As Coding Blocks alumni, you will get priority.

  • A dedicated Placement team along with Hiring Blocks, the placement portal of Coding Blocks work round the clock to ensure the best of opportunities in the Tech arena are available to you.There are more than 150 companies who have partnered with Coding Blocks for hiring our students.

  • Most of the learners at Coding Blocks are College students only. All our courses and curriculum, across Classroom, Live interactive and Online self paced models are created to ensure you can learn while studying in your college.

  • Yes, you will get Certificate of completion once you complete the course. Completion of course depends on how much content has been completed, together with how many assignments have been completed. If you are a dedicated and hard working learner, you can also bag our Certificate of excellence.

  • Our Alumni is our family. We definitely would be more than thrilled to have you back, as this is really common for us. And yes, we would be happy to offer you exciting concessions on fee. We would recommend you to connect to our counsellors who can guide you well on this.