End To End Artificial Intelligence Course

Become an AI Developer. This master-level course is for you if you are looking to learn Artificial Intelligence in and out within a short time!

Note: Enrollments are closed.

Note: Enrollments are closed for March batch,
April batch enrollments will begin soon

Artificial Intelligence in Telugu
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Course Details

3-4 Months

Course Duration


Teaching Language


Mode Of Teaching

March 03, 2022

Start Date

Life Time

Content Access

Fee Stucture


Including GST

Batch Timings

Monday to Friday

07:00 PM – 09:00 PM

Course Syllabus

In this Introduction, you will view the course syllabus to learn what will be taught in this course. You will hear from data science professionals to discover what data science is, what data scientists do, and what tools and algorithms data scientists use on a daily basis. Finally, you will find out why data science is considered the sexiest job in the 21st century.

  • Introduction of python and comparison with other programming languages
  • Installation of Anaconda Distribution and other python IDE
  • Python Objects, Number & Booleans, Strings, Container objects, Mutability of objects
  • Operators – Arithmetic, Bitwise, comparison and Assignment operators, Operators Precedence and
  • Conditions(If else,if-elif-else)
  • Loops(While ,for)
  • Break and Continue statements
  • Range functions
  • String object basics
  • String methods
  • Splitting and Joining strings
  • String format functions
  • List object basics
  • List methods
  • List as Stack and Queues
  • List comprehensions
  • Tuples, Sets, Dictionary object basics, Dictionary
  • Object Methods, Dictionary View Objects, Functions basics, Parameter passing, Iterators
  • Generator Functions
  • Lambda functions
  • Map, Reduce, Filter functions
  • Creating classes and Objects
  • Inheritance, Multiple Inheritance
  • Working with files
  • Reading and writing files
  • Buffered read and write
  • Other File methods
  • Using standard module
  • Creating new modules
  • Exceptions Handling with Try-except
  • Number Guessing
  • Hangman
  • Python Story Generator
  • Calculator
  • Tic-Tac-Toe
  • Plagiarism Checker
  • Matplotlib
  • Seaborn
  • Account creation
  • Pushing Projects
  • Pulling Projects
  • ReadME File
  • Python Pandas – Series
  • Python Pandas – DataFrame
  • Python Pandas – Panel
  • Python Pandas – Basic Functionality
  • Descriptive Statistics
  • Function Application
  • Python Pandas – Reindexing
  • Python Pandas – Iteration
  • Python Pandas – Sorting
  • Working with Text Data
  • Options & Customization
  • Indexing & Selecting Data
  • Statistical Functions
  • Python Pandas – Window Functions
  • Python Pandas – Date Functionality
  • Python Pandas – Timedelta
  • Python Pandas – Categorical Data
  • Python Pandas – Visualization
  • Python Pandas – IO Tools
  • NumPy – Ndarray Object
  • NumPy – Data Types
  • NumPy – Array Attributes
  • NumPy – Array Creation Routines
  • Array From Numerical Ranges
  • NumPy – Indexing & Slicing
  • NumPy – Advanced Indexing
  • NumPy – Broadcasting
  • NumPy – Iterating Over Array
  • NumPy – Array Manipulation
  • NumPy – Binary Operators
  • NumPy – String Functions
  • NumPy – Mathematical Functions
  • NumPy – Arithmetic Operations
  • NumPy – Statistical Functions
  • Sort, Search & Counting Functions
  • NumPy – Copies & Views
  • NumPy – Matrix Library
  • NumPy – Linear Algebra
  • Flask
  • Flask introduction
  • Flask application
  • Open link flask
  • App routing flask
  • URL building flask
  • Http methods flask
  • Templates flask
  • Flask project: portfolio
  • Postman with flask
  • Descriptive and inferential Statistics
  • Sampling Methods
  • Types of Variables
  • Independent and dependent variables
  • Variable Measurement Scales
  • Frequency Distribution and Cumulative Frequency Distribution
  • Bar Graphs and Pie Charts
  • Histograms and stem & leaf plots
  • Arithmetic Mean for samples and populations
  • Central Tendency
  • Variance and Standard deviation for Population and sample
  • Percentiles and Quartiles
  • Inter Quartile Ranges and Box Plots
  • Outliers in data 
  • Skewness for the data
  • The normal curves 
  • Z-scores and z-test for the data
  • Basics of probability
  • Addition Rule
  • Multiplication Rule
  • Permutations
  • Combination 
  • Discrete and Continuous Random Variables
  • Discrete probability distribution
  • Probability Histogram
  • Mean and Expected values of discrete random variables
  • Variance and standard deviation of discrete random variables
  • Binomial distribution
  • Normal distribution
  • Quadrants
  • Pearsons correlation
  • Hypothesis testing with Pearson’s r
  • Spearman correlation 
  • Central Limit theorem 
  • Sample proportions
  • Confidence intervals about the mean, population, standard deviation
  • NULL and alternative Hypotheses
  • Type I and Type II Errors
  • One-Tailed and Two-Tailed Tests
  • What is Data?
  • Difference between CPU and GPU
  • Parallel and sequence processors
  • How data will be arranged in the axis
  • Types of machine learnings
  • What is classification?
  • What is regression?
  • What is clustering?
  • Performance metrics
  • What are errors?
  • What are all the libraries in Machine Learning?
  • Knowing about Tensorflow, Keras, Scikit-Learn, etc.
  • Explorative Data Analysis
  • Bias and variance
  • Linear Regression Maths 
  • Linear Regression building from scratch without libraries
  • Linear Regression Building with Libraries (Scikit Learn)
  • Maths for the Mean Squared Error, Squared Error, Absolute Squared Error.
  • Writing Code from scratch for Mean Squared error 
  • Writing Code from scratch for Squared error 
  • Writing Code from scratch for absolute Squared error 
  • Logistic Regression Maths 
  • Logistic Regression building from scratch without libraries
  • Logistic Regression Building with Libraries (Scikit Learn)
  • Maths for the Accuracy, Precision, Recall, F1-Score
  • Writing Code from scratch for Accuracy
  • Writing Code from scratch for Precision 
  • Writing Code from scratch for Recall
  • Writing Code from scratch for F1-Score
  • Writing code for all the metrics using sklearn (MSE, SE, Accuracy, Precision, etc..)
  • Decision Tree Maths
    • Gini 
    • Entropy
  • Building Decision Tree classifier using python
  • Random Forest Maths
  • Building Random Forest Maths
  • KNN classifier
  • KNN using python
  • SVM with maths
  • SVM building by using python 
  • Voting classifier Maths
    • Harding Voting 
    • Soft Voting
  • Building voting classifier with python
  • Bagging classifier with maths
  • Bagging classifier building with python
  • Ridge Regression with maths
  • Ridge Regression building with python
  • Lasso Regression with maths
  • Lasso Regression building with python
  • SVR with maths
  • SVR building with python
  • Decision Tree Regressor with Maths
  • Decision Tree with python 
  • What is Perceptron?
  • Neurons in humans and AI?
  • What is a single layers perceptron?
  • Neural Networks
  • Hidden Layers
  • Weights and bias
  • Neural networks maths behind it
  • Tensorflow and Keras introduction
  • Building neural networks with TensorFlow
  • Activation functions 
  • Gradient descent algorithms
  • Feedforward network
  • Backpropagation
  • Error and accuracy
  • Stock Price Prediction using Machine Learning
  • Housing Prices Prediction Project
  • Wine Quality Test Project
  • Mall Customers Clustering Analysis
  • CNN introduction
  • Convolutions introduction in humans and AI
  • Padding in CNN
  • Strides in CNN
  • Max pooling in CNN
  • Average pooling in CNN
  • Kernels
  • Features 
  • Math behind CNN 
  • Building CNN with TensorFlow and Keras
  • Training and Testing it
  • What is Corpus?
  • What are Tokens?
  •  What are Engrams?
  • What is Tokenization?
    • What is White-space Tokenization?
    • What is Regular Expression Tokenization?
  • What is Normalization?
    • What is Stemming?
    • What is Lemmatization?
  • Part of Speech tags in NLP
  • Building NLP model with SVM 
  • Maths behind RNN and LSTM
  • Working with RNN
  • Working with LSTM
  • Road Lane line detection – Computer Vision Project in Python
  • text to speech and creating small chatbots
  • Face Detection
  • Flower Detection with pretrained models
  • YOLO Object Detection
  • Creating DB
  • Creating tables
  • Select 
  • Where Clause
  • Having Clause  
  • Operators
  • Aggregate functions
  • Joints
    • Inner
    • Outer
    • Left 
    • Right
  • Introduction
  • Opencv
  • Installations
  • Reading and writing of image
  • Reading and writing or video
  • Drawing shapes in videos and images
  • Writing time date and frames in video
  • Contours
  • Threshold
  • Edges
  • Harcascade files
  • Face Detection
  • Eye Detection
  • Car Detection
  • Bike Detection
  • Face Recognition
  • Face mask  detection


Data Statistical Analysis

Here, you will handle data from the client where their customers are searching for the best house for a low cost Here, you need to do all the analytics on the dataset and help your client suggest a good area for their customers.

Fetal Health Classification

Classify fetal health in order to prevent child and maternal mortality. Reduction of child mortality is reflected in several of the United Nations’ Sustainable Development Goals and is a key indicator of human progress.

Corona Lungs Dataset

Here is the good project, there was a patient that he needs to know he got effected with corona or not by seeing their lungs images. You are as data scientist need to solve the problem for that patient by taking their images and need test them and show them the result.

Model Deployment for Wine Quality data

The main reason for this project out client who is working with wine we need to deploy his project on web because everyone need to use his website and should help them to know the wine quality



Tic Tac Toe is usually played on a three-by-three grid. Each player takes a turn placing a mark in one of the grid’s cells. The object of the game is for players to arrange their marks in such a way that they form a continuous line of three cells, either vertically, horizontally, or diagonally.

S3 BUCKET to Sage Maker

Hey data scientist you’re going to work in a company that which uses a AWS cloud platform to train the model, here you’re going to work on AWS cloud perform your company stores data in S3 bucket and use sage maker to train the models


Lymphography with Neural Network

Lymphography is a medical data where it is having symptoms and different types of lymphography disease, you are going to get the data from hospitals where the patients affected with the lymphography disease, here you are as a data scientist need to classify the patient’s which type of lymphography disease by taking their symptoms from the reports

Classification of Real and Fake images using Computer Vision

Finding the real and fake persons from the images, this type of project is used by international companies to avoid manipulating the real faces with fake. The movie industry used mostly this type of project to change the actor’s duplicates faces with some hero’s face, in this project we used to find fake and real faces from images.

YOLO object detection for the Real-Life objects in real-time

YOLO object detection for the Real-Life objects in real-time

We well know about the Tesla cars, how tesla cars are finding the objects in front of them? Here in this project, you are going to develop a similar type of model which detects all the objects in real-time for example bottles, phones, laptops, chairs. In real-time. YOLO will help to do this type of object detection in real-time. Data will be created by the self and preprocess it then will train on the model.

Face Emotion Recognition

Facial expression recognition software is a system that detects emotions in human faces by using biometric indicators. This technology, more exactly, is a sentiment analysis tool that can recognize the six basic or universal expressions: happy, sorrow, anger, surprise, fear, and disgust, automatically., this is used by most modern companies to find their customers emotions on the products to know their expressions on it.

Tools Covered


What We Provide

Online Community - Codingrad

Online Community

Although our instructors are available 24/7 to clear doubts, we also provide a community where students can post their doubts and also help each other.

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Learning Platform

Along with daily live lectures we provide a separate learning platform where our students can learn topics, attempt quizzes, assignments and many more.

Certificate - Codingrad


Right after completion of the course by submitting the required projects and maintaining attendance, students will receive the course completion certificate. 

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Frequently Asked Questions

The training which CodinGrad provides is Industrial training. We are well known for our course curriculum. And whatever we teach is starts from scratch to advance level. Our well experienced instructors will be available for 24/7 to clear your doubts.

Yes, all the concepts are taught from the basics to the advanced level and our instructors will make sure that the students are understanding or not before going to any futher topics.

Of course, We at CodinGrad train the students according to the industry needs and specification, We also provide in-house projects and mock interviews.

We don’t have any eligibility criteria for our courses as we teach from start to end, thus anybody interested in the course can join.

Yes, you will be receiving a course completion certification from CodinGrad after submitting the projects at the end of the course.

You can enroll by doing the payment from our website and right after payment you will receive the confirmation from our end and we’ll guide for further process.

Yes, all sessions will be recorded and will be provided for the students.