C/C++ Programming Training


“C” is excellent, efficient and general programming language for most of the applications, such as scientific business and system software application. C++ is an Object Oriented Programming Language that is powerful, efficient and compact. It includes concepts like Polymorphism, Dynamic Binding, Data hiding, Operator encapsulation and inheritance, which are to be observed in C. User defined objects (instances) can be reused with and without modifications to generate new application. This reduces coding to greater extent.

Who should attend?

This course is for those interested in programming with C++, including application and systems programmers, software engineers and their managers. Professional programming experience is assumed. C programming experience is not required.

Course Contents For 'C'
  • Basic Structure of C, Constants, Variables, Data types Keywords, Operators, Expression, Conditional Operators.
  • Decision, Loop Switch Control Statement.
  • Arrays String Handling, Creating Functions in C. Introduction to Pointers in C. Passing Pointers as Arguments to Function.
  • Structure, Union, File Handling
  • Dynamic Allocation of Memory.
  • Introduction To Linked List Basics
Course Contents For 'C++'
  • Introduction to Object Oriented Programming (C++).
  • Tokens, expression, data types control structure.
  • Introduction to Classes, Objects,
  • Constructor Deconstruction.
  • Functions in C++.
  • Function overloading, Operator overloading.
  • Inheritance, multiple multilevel inheritance.
  • Introduction to virtual functions, classes polymorphism.
  • File operations using stream classes.
  • Exception Handling.
  • Introduction to Templates.
Machine Learning and Data Science
  • Intro to python
  • Use of data Array 
  • Conditional statements 
  • Functional Programming 
  • Introduction to Python libraries
  • Use of Numpy
  • Data Exploration using pandas, 
  • Demonstration of graphs using Matplotlib
  • Machine learning concept
  • Supervised learning
  • Regression & classification
  • Linear Regression with One Variable, Linear Regression with Multiple Variables
  • Logistic Regression and Regularization 
  • K Nearest Neighbour
  • Decision Trees 
  • Random forest
  • Naïve bayes
  • Support Vector Machines (SVM)
  • Unsupervised Learning 
  • Principle Component Analysis (PCA)
  • K-means Clustering
  • Hierarchical Clustering 
  • Neural networks
  • Hidden Markov model
  • Linear discriminant analysis (LDA)
  • Anomaly Detection and Recommender Systems
  • Markov decision process
  • Practical application of Markov decision process
  • Tabular TD(lambda), replacing traces
  • TD(lambda) with function approximation
  • Introduction to Deep Learning
  • Deep Learning libraries
  • Importance of Tensor Flow
  • Building a computational graph 
  • Programming elements in TensorFlow 
  • Recurrent Neural Networks (RNN)
  • Implementation of RNN using TensorFlow
  • Deep learning
  • Object Detection / face detection using tensor flow 
  • Introduction to Natural Language Processing (NLP)
  • Sentiment Analysis

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