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Artificial Intelligence Certification & Training Course

9999.00 INR 9999.00 INR

Artificial Intelligence Certification & Training Course

Build a successful career in AI with an Artificial Intelligence Course & Certification by JanBask Training. Master industry-valued skills like Python, NLP, Machine learning, Generative AI, prompt engineering, explainable AI, and more.

Course Information

  • Instructor Led Live Classes
  • 100% Job Placement Assistance
  • Comprehensive Curriculum
  • Resume and Interview Guidance
  • Industry-Recognized Certification
  • Flexible Online Learning Options

      • Course Content

    • AI Intro, Python for AI & ML, SQL

      • What is AI, Why AI?
      • Programming for Problem Solving
      • Artificial intelligence fundamentals
      • Components of AI
      • Python Overview
      • Important Python features
      • Python installation, Anaconda Python distribution
      • Python Functions and Packages
      • Scikit-Learn for Machine Learning & LNP
      • Working with Data Structures,Arrays,
      • Math Operators and Expressions
      • Vectors & Data Frames
      • Pandas, NumPy, Matplotlib
      • Numpy for Statistical Analysis
      • Matplotlib & Seaborn for Data Visualization
      • Descriptive Statistics
      • Probability & Conditional Probability
      • Probability Distributions
      • Working with Databases
      • How to create a Database instance on Cloud
      • CREATE Table Statement
      • SELECT Statement
      • COUNT, DISTINCT, LIMIT
      • INSERT, UPDATE and DELETE Statements
      • Information and Data Models
      • Types of Relationships
      • Sub-Queries and Nested Selects

    • Machine Learning: Supervised Learning

      • Introduction to Machine Learning
      • Understanding Supervised Learning.
      • Linear Regression Theory
      • Supervised Learning Regression
      • Linear Regression
      • Multiple Linear Regression
      • Bias-Variance Trade-Off
      • Logistic Regression
      • K-Nearest Neighbors (KNN)
      • Simple Vector Machine (SVM)
      • Decision Trees
      • Bagging
      • Boosting, AdaBoost & XGBoost
      • Naïve Bayes Classifier

    • Machine Learning: Unsupervised Learning

      • K-Means Clustering
      • Hierarchical Clustering
      • Dimensionality Reduction
      • Linear Discriminant Analysis
      • Time Series Modelling
      • Principal Component Analysis (PCA)
      • Reinforcement Learning
      • Model Comparisons
      • Analysis Considerations
      • Clustering Animals
      • Customer Segmentation
      • Optimal Number of Clusters
      • Cluster Based Incentivization
      • Image Segmentation

    • Deep Learning with Tensorflow, Keras

      • What is Neural Networks
      • Gradient Descent
      • Perceptron & Neural Networks
      • Batch Normalization
      • Activation and Loss functions
      • Hyper parameter tuning
      • Deep Neural Networks
      • Tensor Flow & Keras for Neural Networks & Deep Learning
      • Understand Neural Networks in Detail
      • PyTorch Tensors
      • PyTorch Autograd
      • 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

    • Neural Networks, Convolutional Neural Network,Recurrent Neural Networks

      • Artificial neural networks in Deep Learning
      • Linear & Logistic Regression With Tensorflow
      • Activation Functions
      • Illustrate Perceptron
      • Important Parameters of Perceptron
      • Shallow, Deep neural networks
      • Optimization algorithms
      • Hyper-parameter tuning
      • Batch Normalization and Programming Frameworks
      • Single layer NN & Multilayer NN
      • Back propagation, Dynamic Programming
      • Mathematical Take on NN
      • Function Approximator
      • Kernels, Padding & Strides
      • Convolutional Layer, Max-Pooling
      • CNN Architectures – VGGNet, AlexNet, Inception Network
      • Rnns with back propagation
      • Long short-term memory (lstm)
      • Link with Linear Regression
      • Recurrent Neural Network
      • Backpropagation in RNN.
      • Types of RNN, Architecture of RNN

    • Natural Language Processing

      • Understanding NLP
      • Stop Words, Tokenization
      • Stemming and lemmatization
      • Bag of Words Model
      • Word Vectorizer
      • POS Tagging
      • Named Entity Recognition
      • Text Pre-processing
      • Topic Modelling
      • Noise Removal
      • Lexicon Normalization, Lemmatization, Stemming
      • Object Standardization
      • Text Classification, Text Matching
      • Levenshtein Distance
      • Phonetic Matching, Flexible String Matching

    Industry-Recognized AI Certification

    Future-proof your career with a job-oriented AI certification. It's time to get certified!

    Online Course Certification