Introduction to strings, storage of strings and theirinbuilt functions
2D lists, Storage of 2D lists, Example problems using 2D lists
Data Structures & Algorithms
Problem Solving Techniques
Introduction to recursion, Principle of mathematical induction, Fibonacci numbers, Recursion using arrays, Recursion using strings, Recursion using 2D arrays
TIME AND SPACE COMPLEXITY
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.
Object Oriented Programming
BASICS OF OOPS
Introduction to OOPS, Creating objects, Getters and setters, Constructors and related concepts, Inbuilt constructor and destructor, Example classes
ADVANCE CONCEPTS OF OOPS
Static members, Function overloading and related concepts, Abstraction, Encapsulation, Inheritance, Polymorphism, Virtual functions, Abstract classes, Exception handling
Linear Data Structures
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
STACKS AND QUEUES
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
BINARY SEARCH TREES
Introduction to Binary Search Trees, Searching a node in BST, BST class, Inserting and Deleting nodes in BST, Types of balanced BSTs
Advanced Data Structures
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
INTRODUCTION TO DYNAMIC PROGRAMMING
ntroduction to Memoization, Introduction to Dynamic Programming, Fibonacci numbers using recursion, memoization and dynamic programming
APPLICATIONS OF 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
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
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
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
KNN and SVM
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
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
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
TWITTER SENTIMENT ANALYSIS
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
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
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 - 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
Meet the faculty legends that will make you legendary
Co-Founder & Instructor
Love from our alumni
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.
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.
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.
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
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.
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.
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.
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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