C Language Training in KPHB | Best C Course in Kukatpally

C Language

Course Overview

C is one of the most powerful and widely used programming languages. It is the foundation for many modern programming languages and is essential for system programming, embedded systems, and application development. Mastering C helps you understand memory management, data structures, and algorithm development effectively.

Our C Language Training in KPHB is designed for beginners and professionals who want to build strong programming skills. The course covers everything from basic syntax to advanced concepts like pointers, file handling, and memory allocation. With hands-on exercises and real-world examples, you will gain practical coding experience.

At Naresh IT KPHB, we provide expert-led training with a structured curriculum. You will work on projects, solve coding challenges, and learn industry best practices. Whether you are a student or a working professional, this course will help you become proficient in C programming.

Learn software skills with real experts, either in live classes with videos or without videos, whichever suits you best.

Course Details:

Course Price:

with videos : ₹ 4,000/-
with out videos : ₹ 2,000/-

Lesson Duration

45 Days

Language:

English

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Description

The C Language Course in KPHB is a step-by-step program designed to teach core programming concepts. It starts with basic syntax and gradually moves to complex topics like pointers, structures, and dynamic memory allocation. The course includes hands-on coding exercises, assignments, and real-time projects to help learners master C programming.

Our expert trainers will guide you through each concept, ensuring you gain a deep understanding of how C works. You will also learn to debug programs, optimize code performance, and work on practical applications. By the end of the course, you will be able to write efficient and optimized C programs for various applications.

This course is ideal for students, software developers, and anyone looking to start a career in programming. With flexible learning options, including classroom and online training, we ensure you get the best learning experience.

Course Objectives

By enrolling in our C Language Training in KPHB, you will:

Learn the fundamental concepts of C programming, including syntax, variables, and operators.
Understand control structures like loops, functions, and conditional statements.
Master pointers, arrays, structures, and memory management techniques.
Work with file handling and develop efficient algorithms using C.
Debug, test, and optimize C programs for better performance.
Gain hands-on experience through real-time projects and assignments.
Build a strong foundation for learning advanced programming languages like C++, Java, and Python.

Prerequisites

This C Programming Course in KPHB is beginner-friendly and does not require prior coding experience. However, basic knowledge of computers and logic-building skills will be helpful.

No prior programming knowledge is required.
A basic understanding of mathematics and problem-solving skills will be beneficial.
A laptop or computer for practicing C programming exercises.

Course Curriculum

Introduction to Data Science
- Introduction to Data Science
- Discussion on Course Curriculum
- Introduction to Programming
Python Basics
- Introduction to Python: Installation and Running (Jupyter Notebook, .py file from terminal, Google Colab)
- Data types and type conversion
- Variables
- Operators
- Flow Control : If, Elif, Else
- Loops
- Python Identifier
- Building Funtions (print, type, id, sys, len)
Python - Data Types & Utilities
- List, List of Lists and List Comprehension
- List creation
- Create a list with variable
- List mutable concept
- len() || append() || pop()
- insert() || remove() || sort() || reverse()
- Forward indexing
- Backward Indexing
- Forward slicing
- Backward slicing
- Step slicing
Set
- SET creation with variable
- len() || add() || remove() || pop()
- union() | intersection() || difference()
Tuple
- TUPLE Creation
- Create Tuple with variable
- Tuple Immutable concept
- len() || count() || index()
- Forward indexing
- Backward Indexing
Dictionary and Dictionary comprehension
- create a dictionary using variable
- keys:values concept
- len() || keys() || values() || items()
- get() || pop() || update()
- comparision of datastructure
- Introduce to range()
- pass range() in the list
- range() arguments
- For loop introduction using range()
Functions
- Inbuilt vs User Defined
- User Defined Function
- Function Argument
- Types of Function Arguments
- Actual Argument
- Global variable vs Local variable
- Anonymous Function | LAMBDA
Packages
Map Reduce
OOPs
Class & Object
- what is mean by inbuild class
- how to creat user class
- crate a class & object
- __init__ method
- python constructor
- constructor, self & comparing objects
- instane variable & class variable
Methods
- what is instance method
- what is class method
- what is static method
- Accessor & Mutator
Python DECORATOR
- how to use decorator
- inner class, outerclass
- Inheritence
Polymorphism
- duck typing
- operator overloading
- method overloading
- method overridding
- Magic method
- Abstract class & Abstract method
- Iterator
- Generators in python
Python - Production Level
- Error / Exception Handling
- File Handling
- Docstrings
- Modularization
Pickling & Unpickling
Pandas
- Introduction, Fundamentals, Importing Pandas, Aliasing, DataFrame
- Series – Intro, Creating Series Object, Empty Series Object, Create series from List/Array/Column from DataFrame, Index in Series, Accessing values in Series
- NaN Value
- Series – Attributes (Values, index, dtypes, size)
- Series – Methods – head(), tail(), sum(), count(), nunique() etc.,
- Date Frame
- Loading Different Files
- Data Frame Attributes
- Data Frame Methods
- Rename Column & Index
- Inplace Parameter
- Handling missing or NaN values
- iLoc and Loc
- Data Frame – Filtering
- Data Frame – Sorting
- Data Frame – GroupBy
- Merging or Joining
- Data Frame – Concat
- DataFrame - Adding, dropping columns & rows
- DataFrame - Date and time
- DataFrame - Concatenate Multiple csv files
Numpy
- Introduction, Installation, pip command, import numpy package, Module Not Found Error, Famous Alias name to Numpy
- Fundamentals – Create Numpy Array, Array Manipulation, Mathematical Operations, Indexing & Slicing
- Numpy Attributes
- Important Methods- min(),max(), sum(), reshape(), count_nonzero(), sort(), flatten() etc.,
- adding value to array of values
- Diagonal of a Matrix
- Trace of a Matrix
- Parsing, Adding and Subtracting Matrices
- "Statistical Functions: numpy.mean()
- numpy.median()
- numpy.std()
- numpy.sum()
- numpy.min()"
- Filter in Numpy
Matplotlib
- Introduction
- Pyplot
- Figure Class
- Axes Class
- Setting Limits and Tick Labels
- Multiple Plots
- Legend
- Different Types of Plots
- Line Graph
- Bar Chart
- Histograms
- Scatter Plot
- Pie Chart
- 3D Plots
- Working with Images
- Customizing Plots
Seaborn
- catplot() function
- stripplot() function
- boxplot() function
- violinplot() function
- pointplot() function
- barplot() function
- Visualizing statistical relationship with Seaborn relplot() function
- scatterplot() function
- regplot() function
- lmplot() function
- Seaborn Facetgrid() function
- Multi-plot grids
- Statistical Plots
- Color Palettes
- Faceting
- Regression Plots
- Distribution Plots
- Categorical Plots
- Pair Plots
Scipy
- Signal and Image Processing (scipy.signal, scipy.ndimage):
- Linear Algebra (scipy.linalg)
- Integration (scipy.integrate)
- Statistics (scipy.stats)
- Spatial Distance and Clustering (scipy.spatial)
Statsmodels
- Linear Regression (statsmodels.regression)
- Time Series Analysis (statsmodels.tsa)
- Statistical Tests (statsmodels.stats)
- Anova (statsmodels.stats.anova)
- Datasets (statsmodels.datasets)

Set Theory
- Data Representation & Database Operations
Combinatorics
- Feature Selection
- Permutations and Combinations for Sampling
- Hyper parameter Tuning
- Experiment Design
- Data Partitioning and Cross-Validation
Probability
- Basics
- Theoretical Probability
- Empirical Probability
- Addition Rule
- Multiplication Rule
- Conditional Probability
- Total Probability
- Probability Decision Tree
- Bayes Theorem
- Sensitivity & Specificity in Probability
- • Bernouli Naïve Bayes, Gausian Naïve Bayes, Multinomial Naïve Bayes
Distributions
- Binomial, Poisson, Normal Distribution, Standard Normal Distribution
- Guassian Distribution, Uniform Distribution
- Z Score
- Skewness
- Kurtosis
- Geometric Distribution
- Hyper Geometric Distribution
- Markov Chain
Linear Algebra
- Linear Equations
- Matrices(Matrix Algebra: Vector Matrix Vector matrix multiplication Matrix matrix multiplication)
- Determinant
- Eigen Value and Eigen Vector
Euclidean Distance & Manhattan Distance
Calculus
- Differentiation
- Partial Differentiation
- Max & Min
Indices & Logarithms

Introduction
- Population & Sample
- Reference & Sampling technique
Types of Data
- Qualitative or Categorical – Nominal & Ordinal
- Quantitative or Numerical – Discrete & Continuous
- Cross Sectional Data & Time Series Data
Measures of Central Tendency
- Mean, Mode & Median – Their frequency distribution
Descriptive statistic Measures of symmetry
- skewness (positive skew, negative skew, zero skew)
- kurtosis (Leptokurtic, Mesokurtic, Platrykurtic)
Measurement of Spread
- Range, Variance, Standard Deviation
Measures of variability
- Interquartile Range (IQR)
- Mean Absolute Deviation (MAD)
- Coefficient of variation
- Covariance
Levels of Data Measurement
- Nominal, Ordinal, Interval, Ratio
Variable
- Types of Variables.
- Categorical Variables - Nomial variable & ordinal variables
- Numerical Variables: discreate & continuous
- Dependent Variable
- Independent Variable
- Control Moderating & Mediating
Frequency Distribution Table
- Nominal, Ordinal, Interval, Ratio
Types of Variables
- Categorical Variables - Nomial variable & ordinal variables
- Numerical Variables: discreate & continuous
- Dependent Variable
- Independent Variable
- Control Moderating & Mediating
Frequency Distribution Table
- Relative Frequency, Cumulative Frequency
- Histogram
- Scatter Plots
- Range
- Calculate Class Width
- Create Intervals
- Count Frequencies
- Construct the Table
Correlation, Regression & Collinearity
- Pearson & Spearman Correlation Methods
- Regression Error Metrics
Others
- Percentiles, Quartiles, Inner Quartile Range
- Different types of Plots for Continuous, Categorical variable
- Box Plot, Outliers
- Confidence Intervals
- Central Limit Theorem
- Degree of freedom
Bias and Variance in ML
Entropy in ML
Information Gain
Surprise in ML
Loss Function & Cost Function
- Mean Squared Error, Mean Absolute Error – Loss Function
- Huber Loss Function
- Cross Entropy Loss Function
Inferential Statistics
- Hypothesis Testing: One tail, two tail and p-value
- Formulation of Null & Alternate Hypothesis
- Type-I error & Type-II error
- Statistical Tests
- Sample Test
- ANOVA Test
- Chi-square Test
- Z-Test & T-Test

Introduction
- DBMS vs RDBMS
- Intro to SQL
- SQL vs NoSQL
- MySQL Installation
Keys
- Primary Key
- Foreign Key
Constraints
- Unique
- Not NULL
- Check
- Default
- Auto Increment
CRUD Operations
- Create
- Retrieve
- Update
- Delete
SQL Languages
- Data Definition Language (DDL)
- Data Query Language
- Data Manipulation Language (DML)
- Data Control Language
- Transaction Control Language
SQL Commands
- Create
- Insert
- Alter, Modify, Rename, Update
- Delete, Truncate, Drop
- Grant, Revoke
- Commit, Rollback
- Select
SQL Clause
- Where
- Distinct
- OrderBy
- GroupBy
- Having
- Limit
Operators
- Comparison Operators
- Logical Operators
- Membership Operators
- Identity Operators
Wild Cards
Aggregate Functions
SQL Joins
- Inner Join & Outer Join
- Left Join & Right Join
- Self & Cross Join
- Natural Join

EDA
- Univariate Analysis
- Bivariate Analysis
- Multivariate Analysis
Data Visualisation
- Various Plots on different datatypes
- Plots for Continuous Variables
- Plots for Discrete Variables
- Plots for Time Series Variables
ML Introduction
- What is Machine Learning?
- Types of Machine Learning Methods
- Classification problem in general
- Validation Techniques: CV,OOB
- Different types of metrics for Classification
- Curse of dimensionality
- Feature Transformations
- Feature Selection
- Imabalanced Dataset and its effect on Classification
- Bias Variance Tradeoff
Important Element of Machine Learning
Multiclass Classification
- One-vs-All
- Overfitting and Underfitting
- Error Measures
- PCA learning
- Statistical learning approaches
- Introduce to SKLEARN FRAMEWORK
Data Processing
- Creating training and test sets, Data scaling and Normalisation
- Feature Engineering – Adding new features as per requirement, Modifying the data
- Data Cleaning – Treating the missing values, Outliers
- Data Wrangling – Encoding, Feature Transformations, Feature Scaling
- Feature Selection – Filter Methods, Wrapper Methods, Embedded Methods
- Dimension Reduction – Principal Component Analysis (Sparse PCA & Kernel PCA), Singular Value Decomposition
- Non Negative Matrix Factorization
Regression
- Introduction to Regression
- Mathematics involved in Regression
- Regression Algorithms
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Lasso Regression
- Ridge Regression
- Elastic Net Regression
Evaluation Metrics for Regression
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R²
- Adjusted R²
Classification
- Introduction
- K-Nearest Neighbors
- Logistic Regression
- Support Vector Machines (Linear SVM)
- Linear Classification
- Kernel-based classification
- Non-linear examples
- 2 features forms straight line & 3 features forms plane
- Hyperplane and Support vectors
- Controlled support vector machines
- Support vector Regression
- Kernel SVM (Non-Linear SVM)
- Naives Bayes
- Decision Trees
- Random Forest / Bagging
- Ada Boost
- Gradient Boost
- XG Boost
- Evaluation Metrics for Classification
Clustering
Introduction
K-Means Clustering
- Finding the optimal number of clusters
- Optimizing the inertia
- Cluster instability
- Elbow method
Hierarchical Clustering
Agglomerative clustering
DBSCAN Clustering
Association Rules
- Market Basket Analysis
- Apriori Algorithm
Recommendation Engines
- Collaborative Filtering
- User based collaborative filtering
- Item based collaborative filtering
- Recommendation Engines
Time Series & Forecasting
- What is Time series data
- Different components of time series data
- Stationary of time series data
- ACF, PACF
- Time Series Models
- AR
- ARMA
- ARIMA
- SARIMAX
Model Selection & Evaluation
Over Fitting & Under Fitting
- Biance-Variance Tradeoff
- Hyper Parameter Tuning
- Joblib And Pickling
Others
- Dummy Variable, Onehotencoding
- gridsearchcv vs randomizedsearchcv
ML Pipeline
ML Model Deployment in Flask

Introduction
- Power BI for Data scientist
- Types of reports
- Data source types
- Installation
Basic Report Design
- Data sources and Visual types
- Canvas and fields
- Table and Tree map
- Format button and Data Labels
- Legend,Category and Grid
- CSV and PDF Exports
Visual Sync, Grouping
- Slicer visual
- Orientation, selection process
- Slicer: Number, Text, slicer list
- Bin count,Binning
Hierarchies, Filters
- Creating Hierarchies
- Drill Down options
- Expand and show
- Visual filter,Page filter,Report filter
- Drill Thru Reports
Power Query
- Power Query transformation
- Table and Column Transformations
- Text and time transformations
- Power query functions
- Merge and append transformations
DAX Functions
- DAX Architecture,Entity Sets
- DAX Data types,Syntax Rules
- DAX measures and calculations
- Creating measures
- Creating Columns

Deep learning at Glance
- Introduction to Neural Network
- Biological and Artificial Neuron
- Introduction to perceptron
- Perceptron and its learning rule and drawbacks
- Multilayer Perceptron, loss function
- Neural Network Activation function
Training MLP: Backpropagation
Cost Function
Gradient Descent Backpropagation - Vanishing and Exploding Gradient Problem
Introduce to Py-torch
Regularization
Optmizers
Hyperparameters and tuning of the same
TENSORFLOW FRAMEWORK
- Introduction to TensorFlow
- TensorFlow Basic Syntax
- TensorFlow Graphs
- Variables and Placeholders
- TensorFlow Playground
ANN (Artificial Neural Network)
- ANN Architecture
- Forward & Backward Propagation, Epoch
- Introduction to TensorFlow, Keras
- Vanishing Gradient Descend
- Fine-tuning neural network hyperparameter
- Number of hidden layers, Number of neurons per hidden layer
- Activation function
- INSTALLATION OF YOLO V8, KERAS, THEANO
PY-TORCH Library
RNN (Recurrent Neural Network)
- Introduction to RNN
- Back Propagation through time
- Input and output sequences
- RNN vs ANN
- LSTM (Long Short-Term Memory)
- Different types of RNN: LSTM, GRU
- Biirectional RNN
- Sequential-to-sequential architecture (Encoder Decoder)
- BERT Transformers
- Text generation and classification using Deep Learning
- Generative-AI (Chat-GPT)
Basics of Image Processing
- Histogram of images
- Basic filters applied on the images
Convolutional Neural Networks (CNN)
- ImageNet Dataset
- Project: Image Classification
- Different types of CNN architectures
- Recurrent Neural Network (RNN)
- Using pre-trained model: Transfer Learning

Natural Language Processing (NLP)
- Text Cleaning
- Texts, Tokens
- Basic text classification based on Bag of Words
Document Vectorization
- Bag of Words
- TF-IDF Vectorizer
- n-gram: Unigram, Bigram
- Word vectorizer basics, One Hot Encoding
- Count Vectorizer
- Word cloud and gensim
- Word2Vec and Glove
- Text classification using Word2Vec and Glove
- Parts of Speech Tagging (PoS Tagging or POST)
- Topic Modelling using LDA
- Sentiment Analysis
Twitter Sentiment Analysis Using Textblob
- TextBlob
- Installing textblob library
- Simple TextBlob Sentiment Analysis Example
- Using NLTK’s Twitter Corpus
Spacy Library
- Introduction, What is a Token, Tokenization
- Stop words in spacy library
- Stemming
- Lemmatization
- Lemmatization through NLTK
- Lemmatization using spacy
- Word Frequency Analysis
- Counter
- Part of Speech, Part of Speech Tagging
- Pos by using spacy and nltk
- Dependency Parsing
- Named Entity Recognition(NER)
- NER with NLTK
- NER with spacy

Human vision vs Computer vision
- CNN Architecture
- Convolution – Max Pooling – Flatten Layer – Fully Connected Layer
- CNN Architecture
- Striding and padding
- Max pooling
- Data Augmentation
- Introduction to OpenCV & YoloV3 Algorithm
Image Processing with OpenCV
- Image basics with OpenCV
- Opening Image Files with OpenCV
- Drawing on Images, Image files with OpenCV
- Face Detection with OpenCV
Video Processing with OpenCV
- Introduction to Video Basics, Object Detection
- Object Detection with OpenCV
Reinforcement Learning
- Introduction to Reinforcement Learning
- Architecture of Reinforcement Learning
- Reinforcement Learning with Open AI
- Policy Gradient Theory
Open AI
- Introduction to Open AI
- Generative AI
- Chat Gpt (3.5)
- LLM (Large Language Model)
- Classification Tasks with Generative AI
- Content Generation and Summarization with Generative AI
- Information Retrieval and Synthesis workflow with Gen AI
Time Series and Forecasting
- Time Series Forecasting using Deep Learning
- Seasonal-Trend decomposition using LOESS (STL) models.
- Bayesian time series analysis
MakerSuite Google
- PaLM API
- MUM models
Azure ML

Who can learn this course

This course is perfect for:

Students & Freshers – Those who want to start their programming journey with C.
Aspiring Software Developers – Anyone looking to build a career in software development.
Engineering & IT Professionals – Professionals who want to strengthen their programming skills.
Embedded Systems & Hardware Engineers – Those working with microcontrollers and system programming.
Competitive Programmers – Candidates preparing for coding interviews and hackathons.

Enroll Now!

Join our C Language Training in KPHB and start your programming journey with expert guidance and hands-on learning!

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FAQ's

C is a general-purpose, procedural programming language developed by Dennis Ritchie in the early 1970s. It is widely used for system programming, embedded systems, game development, and software development due to its efficiency and control over system resources.

C is important because:

It forms the foundation for modern languages like C++, Java, and Python.
It provides high performance and low-level memory access.
It is used in developing operating systems (Linux, Windows), compilers, and databases.
It is essential for understanding computer architecture and algorithms.

C programming includes fundamental and advanced topics, such as:

Data Types & Variables – int, float, char, double, etc.
Control Statements – if-else, loops (for, while, do-while), switch-case.
Functions – Modular programming using user-defined functions.
Arrays & Strings – Handling collections of data efficiently.
Pointers – Memory management and dynamic memory allocation.
Structures & Unions – Creating complex data types.
File Handling – Reading and writing data to files.
Memory Management – malloc(), calloc(), free() functions.

Mastering these concepts is essential for building efficient and optimized C programs.

C is used in various domains, including:

Operating Systems – Linux, Windows, macOS kernels are written in C.
Embedded Systems – Microcontrollers, IoT devices, and robotics use C for real-time operations.
Game Development – High-performance game engines like Unreal Engine use C/C++.
Database Management Systems – MySQL, PostgreSQL are built using C.
Compilers & Interpreters – C is used to build compilers for languages like Python and Java.
Networking Applications – C is used in socket programming for network communication.

Its speed, portability, and efficiency make it a top choice for system-level programming.

Learning C provides several advantages:

Strong Foundation – Helps understand memory management, data structures, and algorithms.
Portability – C programs can run on multiple platforms with minimal modifications.
Performance – Faster execution compared to high-level languages like Python and Java.
Versatility – Used in embedded systems, OS development, networking, and game development.
Career Growth – C developers are in demand for system programming and hardware-related projects.

By mastering C, you can easily transition to advanced languages like C++, Java, and Python.

C is beginner-friendly, and you don’t need prior programming experience. However, basic knowledge of:

Mathematics & Logic – Helps in understanding programming logic.
Computer Basics – Understanding files, directories, and operating systems.
Problem-Solving Skills – Essential for writing efficient code.

For hands-on learning, using IDEs like Code::Blocks, Turbo C, or Visual Studio Code is recommended.

After learning C, you can apply for various roles, including:

Embedded Systems Engineer – Develops software for microcontrollers and IoT devices.
System Programmer – Works on OS development and hardware interaction.
Game Developer – Uses C/C++ for high-performance game engines.
Software Developer – Builds applications requiring efficient memory management.
Network Programmer – Works on networking protocols and socket programming.
Firmware Developer – Develops low-level system software for devices.

C is a fundamental skill for high-performance computing, robotics, and system-level programming.

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C Language

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