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Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.
Providing the flexibility to learn from our classrooms or anywhere you wish considering these turbulent times.
Technical or Technological, we give you assistance for every challenge you face round-the-clock.
Guiding in & out, until you get placed in your dream job.
An inside look & feel at industry environments by handling real-time projects.
Opportunity to prove your talent as an intern at our partner firms and rope for permanent jobs.
Artificially intelligent systems deal with huge amounts of data and also stores multiple information about multiple entities from multiple sources. All of this appears on the system in a synchronous, or a simultaneous manner
AI-enabled systems are designed to observe and react to their surroundings. They not only perceive the environment and take actions accordingly but also keep in mind the situations that might come up in the near future
AI is helping solve complex quantum physics problems with the accuracy of supercomputers with the help of quantum neural networks. This can lead to path-breaking developments in the near future.
Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.
01 : Return of Multi-Armed Bandit
02 : Higher Level Overviw of Reinforcement Learning
03 : Markov Decision Process
04 : Dynamic Programming
05 : Monte Carlo
06 : Temporal Difference Learning
07 : Approximation Methods
08 : Stock trading Project with Reinforcement Learning
01 : Starting with Python
02 : Interactive Mode, Getting Help, Writing Apps.
03 : Python Elements and Syntax.
04 : Building Your First Python Application.
UNDERSTANDING PYTHON-BUILDING BLOCKS.
05 : Working with Numbers, Text, and Dates
06 : Controlling the Action
07 : Speeding Along with Lists and Tuples.
08 : Cruising Massive Data with Dictionaries
09 : Wrangling Bigger Chunks of Code
10 : Doing Python with Class
11 : Sidestepping Errors
WORKING WITH PYTHON LIBRARIES
12 : Working with External Files
13 : Juggling JSON Data
14 : Interacting with the Internet.
15 : Libraries, Packages, and Modules
01 : Vectors, Matrices, and Arrays
02 : Loading Data
03 : Data Wrangling
04 : Handling Numerical Data
05 : Handling Categorical Data
06 : Handling Text
07 : Handling Dates and Times
08 : Handling Images
09 : Dimensionality Reduction Using Feature Extraction
10 : Dimensionality Reduction Using Feature Selection
11 : Model Evaluation
12 : Model Selection
13 : Linear Regression
14 :Trees and Forests
15 : K-Nearest Neighbors.
16 : Logistic Regression
17 : Support Vector Machines
18 : Naive Bayes
19 : Clustering
20 : Neural Networks
21 : Saving and Loading Trained Models
01 : An Introduction to Data Analysis
02 : Understanding the Nature of the Data
03 : The Data Analysis Process
04 : Quantitative and Qualitative Data Analysis
05 : The NumPy Library
06 : Basic Operations
07 : Indexing, Slicing, and Iterating
Conditions and Boolean Arrays
08 : Array Manipulation
09 : General Concepts
10 : Reading and Writing Array Data on Files
11: pandas: The Python Data Analysis Library
The pandas Library—An Introduction
Test Your pandas Installation
Getting Started with pandas
WORKING WITH PYTHON LIBRARIES
12 : Introduction to pandas Data Structures
13 : Other Functionalities on Indexes
14 : Operations between Data Structures
15 : Function Application and Mapping
Sorting and Ranking
Correlation and Covariance
16 : “Not a Number” Data
17 : Hierarchical Indexing and Leveling
Pandas: Reading and Writing Data
I/O API Tools
CSV and Textual Files
18 : Reading Data in CSV or Text Files
19 : Reading and Writing HTML Files
Reading Data from XML
20 : Reading and Writing Data on Microsoft Excel Files
21 : Pickle—Python Object Serialization
22 : Interacting with Databases
Reading and Writing Data with a NoSQL Database: MongoDB
pandas in Depth: Data Manipulation
23 : Data Preparation
24 : Concatenating
25 : Data Transformation
26 : Discretization and Binning
27 : String Manipulation
28 : Data Aggregation
29 : Group Iteration
Advanced Data Aggregation
Data Visualization with matplotlib
The matplotlib Library
IPython and IPython QtConsole
30 : matplotlib Architecture
31 : pyplot
32 : Using the kwargs
33 : Adding Further Elements to the Chart
34 : Saving Your Charts
Handling Date Values
35 : Line Chart
36 : Bar Chart
37 : Pie Charts
38 : Advanced Charts
39 : mplot3d
40 : Multi-Panel Plots
01 : What is Deep learning?
02 : Machine Learning vs Deep Learning
03 : What is TensorFlow?
04 : Comparison of Deep Learning Libraries
05 : How to Download and Install TensorFlow Windows and Mac
06 : Jupyter Notebook Tutorial
07 : Tensorflow on AWS
08 : TensorFlow Basics: Tensor, Shape, Type, Graph, Sessions & Operators
09 : Tensorboard: Graph Visualization with Example
10 : Scikit-Lear
11 : Linear Regression Tensorflow
12 : Linear Regression Case Study
13 : Linear Classifier in TensorFlow
14 : Kernel Methods
15 : TensorFlow ANN (Artificial Neural Network)
16 : ConvNet(Convolutional Neural Network): TensorFlow Image Classification
17 : Autoencoder with TensorFlow
18 : RNN(Recurrent Neural Network) TensorFlow
01 : Introduction to PyTorch, Tensors, andTensor operations
02 : Probability Distributions Using PyTorch
03 : CNN and RNN Using PyTorch
04 : Introduction to Neural Networks Using PyTorch
05 : Supervised Learning Using PyTorch
06 : Fine-Tuning Deep Learning Models Using PyTorch
07 : Natural Language Processing Using PyTorch
01 : An Introduction to Deep Learning and Keras
02 : Keras in Action
03 : Deep Neural Networks for Supervised Learning
04 : Deep Neural Networks for Supervised Learning
05 : Tuning and Deploying Deep Neural Networks
06 : The Path Ahead
01: Artificial Neural Network
02: Convolutional Neural Network
04: Variational AutoEncoder
05: Implementing the CNN-VAE
06: Recurrent Neural Network
07: Mixture Density Network
08: Implementing the MDN-RNN
09: Reinforcement Learning
10: Deep NeuroEvolution
01 : Extracting the Data
02 : Exploring and Processing Text Data
03 : Converting Text to Features
04 : Advanced Natural Language Processing
05 : Implementing Industry Applications
06 : Deep Learning for NLP
Learn to apply deep learning paradigm to forecast univariate time series data
In this Project We will improve the fire detection system through surveillance cameras by building a model that can not only detect the fire but also the location of the fire to provide effective detection and reporting system for the safety of people
In this Project We will build an artificial intelligence model that can predict the next word that is most likely to come. To implement this we will be using Natural language processing and deep learning
In schools and colleges, a lot of time is wasted in taking the attendance of the students. The idea of the project is to automate the attendance system by using a camera that automatically recognizes the faces and marks the attendance of the people
This project seeks to expand on a pioneering modern application of Deep Learning – facial emotion recognition. Although facial emotion recognition has long been the subject of research and study, it is only now that we are witnessing tangible results of that analysis.
In this project, you will develop a plagiarism detector that can detect the similarities in copies of text and detect the percentage of plagiarism
Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond positionEnroll Now
Enjoy the flexibility of selecting online or offline classes with Osacad first-ever hybrid learning model.
Get the fruitful chance of choosing between the privilege of learning from home or the
advantage of one-on-one knowledge gaining - all in one place.
Why leave the comfort and safety of your home when you can learn the eminent non-technical courses right at your fingertips? Gig up to upskill yourself from home with Osacad online courses.
Exploit the high-tech face-to-face learning experience with esteemed professional educators at Osacad. Our well-equipped, safe, and secure classrooms are waiting to get you on board!
Artificial Intelligence which is a global company with headquarters in Chicago, USA. Artificial Intelligence has partnered with GamaSec, a leading Cyber Security product company. Artificial Intelligence is focusing on building Cyber Security awareness and skills in India as it has a good demand in consulting and product support areas. The demand for which is predicted to grow exponentially in the next 3 years. The Artificial Intelligence training programs are conducted by individuals who have in depth domain experience. These training sessions will equip you with the fundamentalknowledge and skills required to be a professional cyber security consultant.
All graduates of commerce, law, science and engineering who want to build a career in cyber security can take this training.
There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.
You can get started by enrolling yourself. The enrollment can be initiated from this website by clicking on "ENROLL NOW". If you are having questions or difficulties regarding this, you can talk to our counselors and they can help you with the same.
Once you enroll with us you will receive access to our Learning Center. All online classrooms, recordings, assignments, etc. can be accessed here.
4th floor, Khajaguda Main Road, next to Andhra Bank, near DPS, Khajaguda, Gachibowli, Hyderabad, Telangana 500008
Plot No. 430, Sri Ayyappa Society, Khanamet, Madhapur, Hyderabad-500081
Uptown Cyberabad Building, Block-C, 1st Floor Plot – 532 & 533, 100 Feet Road Sri Swamy Ayyappa Housing Society, Madhapur, Hyderabad, Telangana 500081
5999 S New Wilke Rd, Bldg 3, #308 Rolling Meadows, IL 60008