Artificial Intelligence(AI)

Course Detail Image
Course Detail
Course Level: Beginner to Advanced
Course Duration: 4 Months | 8 Months
Training Days: Monday to Friday
Training Time: 4 hours / Day | Regular Office Time
Course Mode: IN-class (Offline) at our premises
Course Type: JOB oriented training
Course Start On: On Registration | Admission
Class Size: 1 to 1 | No Groups| No Batch


  • Considering is your last training: We assure for knowledge, so once your get job then your training will end.
  • Know your skills:Choose/Suggested a technology what you can do best.
  • Authenticate your skills: Entire course is on industrial practice so awarded with experience latter on placement.
  • Be highest paid fresher:We invented a unique model to get the job with highest starting salary, if you get good offer then US, you can join to them.
  • We don’t bind your ability: No specific course content, learn as much as you can, beyond the topics it helps to become logically sound.

Definition of AI and its applications

History and evolution of AI

Types of AI systems (narrow vs. general AI)

Ethical considerations and challenges in AI

As a fresher looking to learn AI, it's important to start with the fundamental concepts and gradually build your skills and knowledge. Here's a suggested course content for an AI training program.

Linear algebra (vectors, matrices, operations)

Calculus (differentiation, integration)

Probability theory and statistics (probability, random variables, distributions, hypothesis testing)

Supervised learning, unsupervised learning, and reinforcement learning Training, validation, and test sets

Performance metrics (accuracy, precision, recall, F1 score)

Overfitting and underfitting

Feature selection and feature engineering

Linear regression

Logistic regression

Decision trees and random forests

Naive Bayes

Support vector machines (SVM)

K-nearest neighbors (KNN)

Clustering algorithms (k-means, hierarchical clustering)

Dimensionality reduction techniques (PCA, t-SNE)

Neural networks and their architecture

Activation functions (sigmoid, ReLU, etc.)

Backpropagation algorithm

Convolutional neural networks (CNN) for image recognition

Recurrent neural networks (RNN) for sequence data

Long short-term memory (LSTM) networks

Generative adversarial networks (GANs)

Transfer learning and fine-tuning

Text preprocessing (tokenization, stemming, lemmatization)

Bag-of-words and TF-IDF representations

Word embeddings (Word2Vec, GloVe)

Recurrent neural networks (RNN) for text processing

Attention mechanisms

Language modeling

Named Entity Recognition (NER)

Sentiment analysis and text classification

Markov Decision Processes (MDPs)

Value iteration and policy iteration

Q-learning and Deep Q-Networks (DQN)

Policy gradients and actor-critic methods

Exploration vs. exploitation trade-off

Computer vision (object detection, image segmentation)

Speech recognition and synthesis

Autonomous systems (self-driving cars, drones)

Recommendation systems

Natural language understanding and chatbots

AI in healthcare, finance, and other industries

Bias and fairness in AI systems

Privacy concerns and data protection

Transparency and interpretability

Algorithmic accountability

AI and job displacement

Implementing machine learning and deep learning algorithms

Building and training neural networks using popular frameworks (TensorFlow, PyTorch)

Solving real-world problems using AI techniques

Analyzing and interpreting AI models' results

Remember that this course outline is just a starting point to Explore in AI, and you can adjust it based on your interests and specific goals within AI. Practical exercises, coding assignments, and projects should be an integral part of the self-learning to reinforce the concepts and gain hands-on experience.

Make a plan about how we can achieve our goal with deadline

Discussed & finalize Project definition

Define difficulties and solutions for project definitions

Research Analytics on project definition

Prepare Documents as : Wireframing, Document of Requirement, Target Audience

Any graduate Can make their career into Front-End development, Web Designing or web developers


No limits on learning, no limits on duration, no limits on salary, no limits on interviews, learn as much as you can & get ready for your first job.


  • 4 months training duration

  • Monday to Friday (04 hours / Day)

  • Only practical based training

  • Individual 1 to 1 training

  • Professional developers as trainer

  • Stipend provide based on performance

  • Confirmed job – on-job training program


  • 8 months training duration

  • Monday to Friday (Regular office time)

  • Live & Direct work with team.

  • Stipend during training, Attractive salary offer.

  • +Unlimited placement, Dual job opportunity.

  • Get your first job offer on the day of joining.

  • IN as fresher OUT as experienced professional developer


  • client
  • client
  • client
  • client
  • client