Artificial Intelligence (Machine Learning + Deep Learning)

Machine Learning is going to shape the future. It is of urgent importance that people aspiring to work in niche roles in IT industry to acquire relevant skills for machine learning and get ahead in learning curve.

  • The InterfaceTM has been continuously focusing on the next frontier of growth in industry and currently has immense expertise across Business Analytics, Data Science, Big Data, Machine Learning, Artificial Intelligence, Deep Learning, Cloud Computing (AWS, Azure and GCP) and more.
  • The InterfaceTM has expert trainers will global experience in machine learning. These trainings are job oriented programs with strong focus on quality, practical’s and 100% placement assistance

Training Inclusions:

  • Training from Industry Expert
  • Study Material
  • Hands-on Lab
  • Training completion certificate
  • 100% Placement Assistance

Artificial Intelligence Program by The Interface Pvt. Ltd.


  • Knowledge in Programming in any traditional programming language like C, C++, Java
  • Preferably knowledge in Python
  • Install Python, Anaconda with Jupyter as the preferred IDE

Good to have:

  • Basic knowledge of Data Science and Python


Program schedule: Once the PythonTraining is over we will commencewith the below topics for Artificial Intelligence

  • Introduction to Machine Learning and Artificial Intelligence
  • Supervised and Unsupervised learning, a peek into Reinforcement Learning
  • Real life use-cases - the latest trends.
  • Understanding participant’s knowledge in AI,ML
  • Introduction to ‘Python’ & 'R' – overview of basic programming for AI, ML
  • Hands On - basic data structures in R, Python
  • Emphasis on Python hands on for AI, ML
  • Data pre-processing exercise

Assignment 1: Prepare the data for Machine Learning

  • Introduction to Regression and Classification
  • Concepts of Overfitting, Regularization
  • Different ML models - Decision Trees, Naive Bayes, k-NN
  • Hands on of the above models in Python

Assignment 2: Classification algorithm in Python

  • Artificial Neural Network
  • Intuition
  • Neural Net - architecture
  • Understanding when to use Neural Networks
  • Activation Functions
  • Neural Nets – Feedforward and Backward propagation
  • Hands on Neural Network

Assignment 3: Project work on Neural Network Participants should be familiar with ML and do projects in Python for the same

  • Introduction to Deep Learning
  • Understand when to use Deep Neural Nets
  • Advanced Activation functions in Deep Learning - Softmax, ReLU
  • Introduction to Tensorflow and Keras frameworks - Hands on

Assignment 4: Build a simple NLP model with Tensorflow

  • Introduction to Natural Language Processing(NLP)
  • Understand when to use NLP
  • Text Preprocessing
  • Lemmatization, Stemming
  • Syntactical Parsing, Entity Parsing
  • Hands-on of Text Preprocessing
  • Topic Modelling with Latent Dirichlet Allocation(LDA)
  • Gibbs Sampling
  • Word Embedding/li>
  • Hands on for Topic Modelling /li>
  • Text generation with NLP/li>
  • Hands on in Tensorflow for Text Generation/li>