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

Pre-requisite:

• 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

Syllabus:

• 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