AI-ML Engineer - Rooman Technologies

AI-ML Engineer

AI-ML Engineer- A fast track to the success.
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10654 Learners Enrolled

Program Duration : 320 Hrs

At 15 - 20 hrs/week

Classroom Based

Learning format

Branches Across India

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About Program

Machine learning & AI programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand, on the computer’s part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. “In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step”.

Audience

  • B.E./ B.Tech. – Computer Science, IT, Electronics or equivalent
  • MCA, MBA or Masters in Quantitative Subject
  • Working Professional in IT aspiring to start a career in Data Science

Key Benefits

  • Data Architecture
  • Data Management
  • Data Quality
  • Statistics – Basic & Advanced
  • Machine Learning
  • Time Series
  • Text Analytics
  • Data Visualization

Course Curriculum

  • Element of Reinforcement Learning
  • Models, Values and Policies
  • Markov Chains
  • Monte Carlo Methods
  • Temporal Difference
  • Robotics and Gym Environment
  • AutoEncoders
  • GANs
  • Attention Mechanism
  • Artificial Neural Networks In Python
  • Activation Functions
  • Optimizers
  • Tensorflow / Keras
  • Pytorch / Lightning
  • Types of Deep Learning Algorithms
  • Computer Vision
  • Transfer Learning
  • CNN Based Architectures
  • Recurrent Neural Networks
  • Natural Language Processing
  • Unix based OS (Ubuntu)
  • Containerization
  • Django Web Framework
  • AWS
  • Github
  • Regularization
  • Logistic Regression
  • Support Vector Machine (SVM)
  • Time Series
  • Cross-Validation Techniques
  • Evaluation Metrics
  • Ensembling
  • Unsupervised Learning
  • Clustering
  • Dimensionality Reduction
  • Introduction to Machine Learning
  • EDA and Data Wrangling
  • Scaling and Preprocessing
  • Feature Selection and Extraction
  • Modelling and Pipelining
  • Regression Analysis
  • Polynomial Regression
  • Decision Tree
  • K Nearest Neighbors
  • Naïve Bayes
  • Statistics
  • Hypothesis Testing
  • Power BI
  • R Language
  • Basic Language
  • Advance Concepts
  • Data Structures
  • Numpy
  • Data Manipulation with Pandas
  • Data Visualization

Tools & Softwares

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