Java A to Z : Full Stack Development

From a beginner to expert. Beat everyone !
Online batch starting on 5th January
LEARN.INSPIRE.GROW.

To be a part of the changing trends, one should be aware of the core concepts and the latest advancements including aspects of Supervised, Unsupervised and the very latest and riveting field of Deep Learning. The only thing that stops students from venturing in the field is the seemingly endless nature of the Machine Learning training course and the focus on theory and tedious maths. We promise to deliver a curated experience that focuses on concept rather than concentrating on just theory with a strong boost of coding and projects that will enable one to tackle any problem and explore new areas of research and applications in Machine Learning.

Sessions by Coding Club Team

Rishabh's Short Story

More Sessions

Course Contents
All Sessions :2-3 months
  • Setup and Installation
      OS Requirement : Setting up linux
      IDE Installation : Pycharm
      Anaconda and Jupyter Notebook Installations
  • Python Fundamentals
      Variables, Data Types, literals
      Operators, Conditions, Loops
  • Python Functions and Data Structures
      List, Set, Dictionary, Tuple, String
      Functions, Lambda Expression, Decorators
  • Intermediate Python
      OOPS, Modules, File Handling
      Exception handling, Iterators, Generators
      Python Practice Problems
  • Python Libraries
      Statistical Computation in Python (Numpy, Scipy)
      Data Visualisation (Pandas, Matplotlib, Seaborn, Plotly)
  • Linear Algebra Concepts and Calculus
      Vectors, Matrices, Determinants
      Eigen Values, Eigen Vectors
      Linear Transformations, Calculus Basics
  • Probability and Statistics Fundamentals
      PDF,CDF,Z Value and Distributions
      Confidence Interval, Hypothesis Testing
  • Dimensionality Reduction and Visualisation
      Principal Component Analysis
      Visualizations of complex datasets
      T-Distributed Stochastic Neighbourhood Embedding
  • Machine Learning Introduction
      Basics, Types of Machine Learning
      Data Generation, Visualisation & Collection Hands-on
  • Regression Techniques
      Linear Regression, Gradient Descent
      Multivariate and Locally Weighted Regression
      Closed Form Solution
  • Logistic Regression
      Likelihood Estimation and Loss
      Project 1- Classifying Handwritten Digits using MNIST Dataset
  • K-Nearest Neighbours
      Implementing Algorithm
      Project 2- Real Time Face Recognition using KNN
  • Natural Language Processing
      NLTK, Stop word Removal, Stemming
      Bag of Words, TF-IDF
      Project 3- Text Based Recommendation System on Amazon Real Data
  • Naive Bayes Classifier
      Bayes Theorem Proof and Implementing Algorithm
      Project 4- Movie Rating Prediction using Naive Bayes
  • K-Means Clustering
      Implementing Algorithm
      Project 5- Image Segmentation using K-Means
  • Support Vector Machines
      Handling outliers, Kernels
      Project 6- Image Classification using SVM
  • Neural Nets
      ANN, Backpropagation, One hot encoding
      Convolutional Neural Networks
  • Deep Learning Fundamentals and Wrap up
      Transfer Learning(CS)
      Markov Chains
      RNN's and LSTM
Ask doubts  on live session or via daily discussion on batch whatsapp group can be done with team  anytime. Additional recorded sessions will be provided for practice too along with live sessions.

Course Schedule


Online Live Sessions + Recorded Sessions + Assignments
Online Live Sessions Start Date End Date Day & Time
Each Session : 90 mins 5th January2-3 months2 sessions per week



Additional recorded sessions will be provided for practice too along with live sessions. Each session will be live so that you can ask doubts and same session will be available as recorded stream till course deadline + extra 3 weeks. Ask doubts on live session and daily discussion can be done with team on batch group.

Instructor and Teaching Assistants

Java A to Z : Full Stack Development

About the Instructor

Rishabh Jain

Instructor

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Rishabh has a Bachelor's degree in Software Engineering from DTU. He is a code enthusiast with previous experience at Media.net @ Directi for 3.5 years. He has been in into tech industry for 6 years. Rishabh has been working on his own ventures in AdTech, Media and Education Industry. He loves to Mentor people.

Check Teaching Assistants

Language

English

Commitment

3-5 hours/week

Course Duration

2-3 months

What Students Say About Us ?

Actually Its like a family. Its a club for coders where we from different colleges meet discuss on problems.Its like a place where we can come, discuss and enjoy.There are great Alumni's to guide us all through.The classes being conducted by the alumni are definitely awesome for boosting our CV.

Pranav Malik, DTU

Coding club is a great community which provides interface with some brilliant minds. It provides us with resources and guidance regarding your interests and motivates you to take up new challenges everyday.

Mukan Sakyavar, IGDTUW

Coding club is a awesome place for explorers and learners. Personally I have seen whenever I go to Coding club I ended by discussing a awesome coding question asked in Campanies like Directi,Paytm etc. So that's what I love about it. We solve challenging questions there. I got to know new approach to challenging questions given by people who are Ex-Directi,Paytm etc

Jatin Bindra, MSIT

It is a great community built for great coders. I wish I'd come here earlier. This is by far the best decision I've made. It gave me the platform to learn so much, to meet new people, and most importantly to develop the approach one needs while coding. The healthy environment here is the best I've seen. Happy to be with like minded people.

Abhishek Gupta, MAIT

Good institute,Concepts were taught the best way possible and Rishabh bhaiya is a great mentor.

Aman Jain, BVCOE

Check Photos: Coders @ Coding Club
Posted by Coding Club on Sunday, 16 December 2018

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