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Duration

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3 months

5/5
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Become job-ready by mastering the advanced concepts of Oracle Fusion Financials. Our  Oracle Fusion Financials training course covers all the concepts from basics to advanced levels including ledger, account payables & receivables, assets, identity manager, and more through real-time use cases. You will also get an exposure to industry based real-time projects which are in line with the Oracle Fusion Financials certification exam.

KEY FEATURES

30 Hrs Instructor Led Training

30 Hrs Instructor Led Training

30 Hrs Instructor Led Training

30 Hrs Instructor Led Training

30 Hrs Instructor Led Training

30 Hrs Instructor Led Training

30 Hrs Instructor Led Training

30 Hrs Instructor Led Training

CHOOSE THE TRAINING THAT’S BEST FOR YOU

SELF-PACED E-LEARNING

  • Learn at your convenient time and pace
  • Gain on-the-job kind of learning experience through high quality Oracle Fusion Financials videos built by industry experts.
  • Learn end to end course content that is similar to instructor led virtual/classroom training.
  • Explore sample Oracle Fusion Financials training videos before signing up.
₹ 15000

LIVE ONLINE TRAINING

  • Live demonstration of features and practicals.
  • Gain on-the-job kind of learning experience through high quality Oracle Fusion Financials videos built by industry experts.
  • Learn end to end course content that is similar to instructor led virtual/classroom training.
  • Explore sample Oracle Fusion Financials training videos before signing up.
₹ 30000

CORPORATE TRAINING

  • Learn at your convenient time and pace
  • Gain on-the-job kind of learning experience through high quality Oracle Fusion Financials videos built by industry experts.
  • Learn end to end course content that is similar to instructor led virtual/classroom training.
  • Explore sample Oracle Fusion Financials training videos before signing up.

HEADING

What does this Course Cover?

  • Python Programming and Data manipulations
  • Data Cleaning and Preparation
  • Basic Statistics
  • Basic Machine Learning Algorithms
  • Advanced Machine Learning Algorithms
  • Model building, validating and finetuning
  • Feature engineering techniques
  • Tips and Tricks in ML projects

Target Audience for this Course

  • Everybody who wants to get started with machine learning & deep learning
  • Reporting analysts who aim to become data scientists.
  • Predictive modeling profile candidates who want to learn ML and DL
  • Data visualization experts
  • Any Data science aspirants
  • Graduates and undergraduate students
  • Computer Science Engineering students

What Will You Learn in the Course?

  • How to apply machine learning to real-life business problems
  • How to build ML models and draw useful inferences from them
  • How to write Python programs and how to handle data in Python
  • How to perform fundamental descriptive analysis to gain insights from the data
  • How to explore, validate, and clean data in a systematic way
  • How to find associations between variables
  • How to predict a dependent variable using several independent factors
  • How to validate and finetune machine learning models

Key Features of this Course

  • 100% hands-on sessions
  • Learning through problem-solving and case studies
  • Continuous assessments and Feedback
  • Access to recorded videos
  • Taught by industry professionals with many years of experience in the field
  • It contains numerous examples right from the first lecture until the end.
  • Every concept is explained using a business scenario or case study.
  • It offers the right mix of theory and hands-on labs.
  • The course simplifies complex statistical concepts.

Pre-Requisites

  • This program is the first course in machine learning. There is no strict pre-requisite
  • Anyone with a primary degree can get started with this course
  • Basic mathematical skills are sufficient
  • Statistical Knowledge is NOT a prerequisite. It will be taught in class.
  • Cutting-edge programming knowledge is NOT a pre-requisite. Coding will be taught in the class.

Duration:

  • 3 Live Sessions Classes every week for 3 months
  • Each session is 4 hours

Lab Setup:

  • Windows 10 with 8 GB RAM.
  • Proxy-free internet, Admin rights to execute scripts
  • Firefox and Chrome browsers
  • Access to Google colab notebooks – https://colab.research.google.com/

How do we measure the impact of the training?

  • Post-training assessment for all the participants.
  • Project assignments after each module
  • Multiple quizzes in each module.

Course Delivery Plan

  • The training has four major phases in each concept discussion:
  • Theory, Demo, in-class exercise, and project assignment.
  • The algorithm theory will be explained first. The instructor will take a dataset and demonstrate the concept on a dataset. In the third phase, participants will try the code on a new dataset. In the fourth phase, a real-time dataset will be considered and floated as a project.

Course Currilcum

    • MLPy Live Module 1: Python Programming for Machine Learning 7 days
      • Introduction to Machine Learning and Data Science
      • Python Programming Basics
      • Data Handling in Python
      • Basic Statistics
      • Mean, Median, Variance
      • Percentiles
      • Data Exploration, Validation and Cleaning
    • MLPy Live Module 2 : Supervised Machine Learning 2 weeks
      • Regression Analysis and Simple Regression
      • Multiple Regression and Multi-collinearity
      • Logistic Regression Model Building
      • Decision Trees
      • Information gain
      • Over-fitting
      • Pruning
      • Model Validation and Selection
      • Bias Variance Trade-off
      • Feature Engineering
    • MLPy Live Module 3 : Unsupervised Learning, Ensemble Learning and Boosting 2 weeks
      • Unsupervised learning
        • Cluster Analysis
      • Ensemble Learning
        • Bagging
      • Random Forests
        • Hyperparameters in Random Forests
      • Gradient Boosting
        • Hyperparameters in Gradient Boosting
    • MLPy Live Module 4 : Artificial Neural Networks and Deep Learning 7 days
      • Artificial Neural networks
      • Concept of the decision boundary
      • The non-linear decision boundary
      • Gradient Descent and Backpropagation Algorithm
      • Deep Learning Introduction
        • CNN and RNN
    • MLPy Live Module 5 : Natural Language Processing 7 days
      • Text mining Basics
      • Preparing text for Analysis
      • Naive Bayes Model and Sentiment Analysis
      • Word2Vec Algorithm
    • MLPy Live Module 6 : Final Project and Assessment 2 weeks
      • Additional Projects
      • Assessments
      • Documentation of the projects
      • Profile building

Statinfer

Statinfer derived from Statistical inference is a company that focuses on the data science training and R&D.We offer training on Machine Learning, Deep Learning and Artificial Intelligence using tools like R, Python and TensorFlow

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