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Artificial Intelligence(AI) Training in Hyderabad

The career transformation is guaranteed by this advanced certification in artificial intelligence course in Hyderabad. This is an opportunity to learn with the finest artificial intelligence course for a limited time. To meet the huge demand for this knowledge, learn chatbot skills, tools, and operations required to be certified in artificial intelligence.

35 Modules

with Certifications


After Completion



Artificial intelligence:

In this field, your Artificial intelligence Knowledge will improve its ability to understand, analyse , and transform information so as to strengthen the growth of your business. This AI course can help you move forward in leaps and bounds in order to drive data and achieve significant results. Your career will be accelerated by this certified AI training, which covers the relevant topics, and pushes you to work on real-world problems.

  • Artificial intelligence AI is the intelligence of machines and software, compared to that of humans or animals. 
  • It is the subject of a computer science branch of study that produces and researches intelligent devices. 
  • The smart machines themselves are also known as AI. Artificial intelligence replicates the functions of human intelligence, especially in computer systems. 
  • Some of the specialized uses of AI include machine learning, speech recognition, natural language processing, expert systems, and vision.
  • In this Comprehensive Artificial Intelligence training course in Hyderabad, you will learn the art of solving complex problems using AI implementations such as reinforcement Learning, Computer Vision, OpenCV, and Chatbots.
  •  Through a range of industry projects performed by expert instructors, the intensive course offers an excellent basis for Artificial Intelligence and Machine Learning. 
  • You will learn how to create AI programs from the basic to the advanced level for data analysis and image recognition with our online AI certification training courses.

Our overall development philosophy is based on a solid foundation that believes in the combination of theoretical knowledge and practical training. As a result, we are the top Artificial Intelligence course in Hyderabad.

Topics You will Learn

  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence
  • What is Data Analysis
  • What is Data Mining
  • Value Chain
  • Types of Analytics
  • Lifecycle Probability
    Analytics Project
  • Lifecycle
    Advantage of Deep
  • Learning over Machine learning
  • Reasons for Deep Learning
    Real-Life use cases of
  • Deep Learning
    Review of Machine Learning
  • Basis of Data
  • Categorization
  • Types of Data
  • Data Collection Types
  • Forms of Data &
  • Sources
  • Data Quality & Changes
  • Data Quality Issues
  • Data Quality Story
  • What is Data Architecture
  • Components of Data Architecture
  • OLTP vs OLAP
  • How is Data Stored
  • What is Big Data
    Vs of Big Data
  • Big Data Architecture
  • Big Data Technologies
  • Big Data Challenge
  • Big Data
  • Requirements
    Big Data Distributed
  • Computing & Complexity
  • Hadoop
  • Map Reduce Framework
    Hadoop Ecosystem
  • Data Science Deep Dive
  • What Data Science is
  • Why Data Scientists are in demand
  • What is a Data Product
  • The growing need for Data Science
  • Large Scale Analysis Cost vs Storage
  • Data Science Skills
    Data Science Use Cases
  • Data Science Project Life Cycle & Stages
  • Data Acuqisition
    Where to source data Techniques
  • Evaluating input data
    Data formats
  • Data Quantity Resolution Techniques
  • Data Transformation
    File format Conversions
  • Python Overview
  • About Interpreted Languages
  • Advantages/Disadvantages of Python pydoc.
  • Starting Python Interpreter PATH
  • Using the Interpreter
    Running a Python Script
  • Using Variables
  • Built-in Functions
  • Strings Different
  • Literals
  • Math Operators and
  • Expressions
  • Writing to the Screen
  • String Formatting
  • Command Line
  • Parameters and Flow
  • Control.
  • Lists
  • Tuples
  • Indexing and Slicing
  • Iterating through a
  • Sequence
  • Functions for all
  • Sequences
  • The x range() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets.
  • Learning Num Py
    Introduction to Pandas
  • Creating Data Frames
    Plotting Data
  • Creating Functions
    Slicing/Dicing Operations.

• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values. Sorting
• Alternate Keys
• Lambda Functions
• Sorting Collections of Collections
• Classes & OOPs

• What is Statistics
• Descriptive Statistics
• Central Tendency Measures
• The Story of Average
• Dispersion Measures
• Data Distributions
• Central Limit Theorem
• What is Sampling
• Why Sampling
• Sampling Methods
• Inferential Statistics

  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques
  • Similarity Metrics
  • Distance Measure Types: Euclidean, Cosine Measures
  • Creating predictive models
  • Understanding K-Means Clustering
  • Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
  • Case study
  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Recommendation Use-case
  • Case study
  • How to build Decision trees
  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Decision Tree
  • Confusion Matrix
  • Case study
  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance
  • Various approaches to solve a Data Science Problem
  • Pros and Cons of different approaches and algorithms.
  • Case study
  • Introduction to Predictive Modeling
  • Linear Regression Overview
  • Simple Linear Regression
  • Multiple Linear Regression
  • Case study
  • Logistic Regression Overview
  • Data Partitioning
  • Univariate Analysis
  • Bivariate Analysis
  • Multicollinearity Analysis
  • Model Building
  • Model Validation
  • Model Performance Assessment AUC & ROC curves
  • Scorecard
  • Case Study
  • Introduction to SVMs
  • SVM History
  • Vectors Overview
  • Decision Surfaces
  • Linear SVMs
  • The Kernel Trick
  • Non-Linear SVMs
  • The Kernel SVM
  • Describe Time Series data
  • Format your Time Series data
  • List the different components of Time Series data
  • Discuss different kind of Time Series scenarios
  • Choose the model according to the Time series scenario
  • Implement the model for forecasting
  • Explain working and implementation of ARIMA model
  • Illustrate the working and implementation of different ETS models
  • Forecast the data using the respective model
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective model for forecasting
  • Visualizing and formatting Time Series data
  • Plotting decomposed Time Series data plot
  • Applying ARIMA and ETS model for Time Series forecasting
  • Forecasting for given Time period
  • Case Study
  • Various machine learning algorithms in Python
  • Apply machine learning algorithms in Python
  • How to select the right data
  • Which are the best features to use
  • Additional feature selection techniques
  • A feature selection case study
  • Preprocessing
  • Preprocessing Scaling Techniques
  • How to preprocess your data
  • How to scale your data
  • Feature Scaling Final Project
  • Highly efficient machine learning algorithms
  • Bagging Decision Trees
  • The power of ensembles
  • Random Forest Ensemble technique
  • Boosting – Adaboost
  • Boosting ensemble stochastic gradient boosting
  • A final ensemble technique
  • Introduction Model Tuning
  • Parameter Tuning GridSearchCV
  • A second method to tune your algorithm
  • How to automate machine learning
  • Which ML algo should you choose
  • How to compare machine learning algorithms in practice
  • Sentimental Analysis
  • Case study
  • Introduction to Spark Core
  • Spark Architecture
  • Working with RDDs
  • Introduction to PySpark
  • Machine learning with PySpark – Mllib
  • Case Study
  • Deep Learning Overview
  • The Brain vs Neuron
  • Introduction to Deep Learning
  • The Detailed ANN
  • The Activation Functions
  • How do ANNs work & learn
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropogation
  • Understand limitations of a Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlow
  • Building a multi-layered perceptron for classification
  • Why Deep Networks
  • Why Deep Networks give better accuracy?
  • Use-Case Implementation
  • Understand How Deep Network Works?
  • How Backpropagation Works?
  • Illustrate Forward pass, Backward pass
  • Different variants of Gradient Descent
  • Convolutional Operation
  • Relu Layers
  • What is Pooling vs Flattening
  • Full Connection
  • Softmax vs Cross Entropy
  • ” Building a real world convolutional neural network
  • for image classification”
  • Recurrent neural networks rnn
  • LSTMs understanding LSTMs
  • long short term memory neural networks lstm in python
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Building a Autoencoder model
  • Introducing Tensorflow
  • Introducing Tensorflow
  • Why Tensorflow?
  • What is tensorflow?
  • Tensorflow as an Interface
  • Tensorflow as an environment
  • Tensors
  • Computation Graph
  • Installing Tensorflow
  • Tensorflow training
  • Prepare Data
  • Tensor types
  • Loss and Optimization
  • Running tensorflow programs
  • Tensors
  • Tensorflow data types
  • CPU vs GPU vs TPU
  • Tensorflow methods
  • Introduction to Neural Networks
  • Neural Network Architecture
  • Linear Regression example revisited
  • The Neuron
  • Neural Network Layers
  • The MNIST Dataset
  • Coding MNIST NN
  • Deepening the network
  • Images and Pixels
  • How humans recognise images
  • Convolutional Neural Networks
  • ConvNet Architecture
  • Overfitting and Regularization
  • Max Pooling and ReLU activations
  • Dropout
  • Strides and Zero Padding
  • Coding Deep ConvNets demo
  • Debugging Neural Networks
  • Visualising NN using Tensorflow
  • Transfer Learning Introduction
  • Google Inception Model
  • Retraining Google Inception with our own data demo
  • Predicting new images
  • Transfer Learning Summary
  • Extending Tensorflow
  • Keras
  • TFLearn
  • Keras vs TFLearn Comparison

Let Your Certificates Speak


All You Need to Start this Course


Still Having Doubts?

Artificial Intelligence is the creation of computer programs that can carry out tasks that normally require human intelligence. Speech recognition, problem-solving, learning, and decision-making are some of these tasks.

Three main categories apply to AI: Superintelligent AI, General or Strong AI, and Narrow or Weak AI. While General AI is capable of carrying out every intellectual task that a human can, Super intelligent AI surpasses human intelligence in every way. Narrow AI is tailored for a single task.

Using a labeled dataset, supervised learning entails training a model so that the algorithm can understand the relationship between input and output. Unsupervised learning works with data that hasn't been labeled; the algorithm figures out relationships or patterns on its own.
A branch of artificial intelligence called "machine learning" focuses on creating algorithms that let computers learn from data and get better over time at a particular task.

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