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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
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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
Introduction to Deep Learning & AI
- Deep Learning: A revolution in Artificial Intelligence
- Limitations of Machine Learning
What is Deep Learning?
- Need for Data Scientists
- Foundation of Data Science
- What is Business Intelligence
- What is Data Analysis
- What is Data Mining
What is Machine Learning Analytics vs Data Science ?
- 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
Data AI
- 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
Big Data
- 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
- 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
Annonymization
Python
- Python Overview
- About Interpreted Languages
- Advantages/Disadvantages of Python pydoc.
- Starting Python Interpreter PATH
- Using the Interpreter
Running a Python Script - Using Variables
Keywords - 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
Operators and Keywords for Sequences
- The x range() function
- List Comprehensions
- Generator Expressions
- Dictionaries and Sets.
Numpy & Pandas
- Learning Num Py
Introduction to Pandas - Creating Data Frames
GroupingSorting
Plotting Data - Creating Functions
Slicing/Dicing Operations.
Deep Dive – Functions & Classes & Oops
• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values. Sorting
• Alternate Keys
• Lambda Functions
• Sorting Collections of Collections
• Classes & OOPs
Statistics
• 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
Machine Learning, Deep Learning & AI using Python
- ML Fundamentals
- ML Common Use Cases
- Understanding Supervised and Unsupervised Learning Techniques
Clustering
- 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
Implementing Association rule mining
- What is Association Rules & its use cases?
- What is Recommendation Engine & it’s working?
- Recommendation Use-case
- Case study
Understanding Process flow of Supervised Learning Techniques Decision Tree Classifier
- 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
Random Forest Classifier
- What is Random Forests
- Features of Random Forest
- Out of Box Error Estimate and Variable Importance
Project Discussion Problem Statement and Analysis
- Various approaches to solve a Data Science Problem
- Pros and Cons of different approaches and algorithms.
Linear Regression
- Case study
- Introduction to Predictive Modeling
- Linear Regression Overview
- Simple Linear Regression
- Multiple Linear Regression
Logistic 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
Support Vector Machines
- Case Study
- Introduction to SVMs
- SVM History
- Vectors Overview
- Decision Surfaces
- Linear SVMs
- The Kernel Trick
- Non-Linear SVMs
- The Kernel SVM
Time Series Analysis
- 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
Machine Learning Project
- Various machine learning algorithms in Python
- Apply machine learning algorithms in Python
Feature Selection and Pre-processing
- 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
Which Algorithms perform best
- 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
Model selection cross validation score
- 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
Text Mining& NLP and PySpark and MLLib
- Sentimental Analysis
- Case study
- Introduction to Spark Core
- Spark Architecture
- Working with RDDs
- Introduction to PySpark
- Machine learning with PySpark – Mllib
Deep Learning & AI using Python
- Case Study
- Deep Learning Overview
- The Brain vs Neuron
- Introduction to Deep Learning
Introduction to Artificial Neural Networks
- 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 Neural Networks
- 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”
What are RNNs – Introduction to RNNs
- Recurrent neural networks rnn
- LSTMs understanding LSTMs
- long short term memory neural networks lstm in python
Restricted Boltzmann Machine (RBM) and Autoencoders
- Restricted Boltzmann Machine
- Applications of RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Building a Autoencoder model
Tensorflow with Python
- 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
Building Neural Networks using Tensorflow
- 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
Deep Learning using Tensorflow
- 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 using Keras and TFLearn
- 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
- Artificial Intelligence Professional Certificate Program.
- Certificates are globally recognized & they upgrade your programming profile.
- Certificates are generated after the completion of course.
All You Need to Start this Course
- A computer/mobile device with an internet connection.
- A combination of technical expertise, real-world experience, and educational background is needed to become proficient in artificial intelligence (AI). Before delving into artificial intelligence, consider the following requirements.
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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.
