Machine Learning

4,500.00

Course Overview

This Machine Learning course curriculum is an intensive application-oriented, real-world scenario-based program using Python & Machine Learning. This course is a 60 hours program, intensive skill-oriented, practical training program required for building ML-based models. It is designed to give the participant enough exposure to the variety of applications that can be built using techniques covered under this program. This course is designed for professionals from a variety of IT backgrounds.

Key Learning Outcomes:

  • Stay Industry-relevant and grow in your career.
  • Create ML solutions for various business problems.
  • Understand how to apply ML practices in real-world scenarios
  • Define effective objectives for ML projects.
  • Work with different types of data.
  • Build and deploy production-grade ML applications.

Duration

200 hours (80 hours ONLINE LIVE sessions + 120 Hours of assignment)

Schedule

  • 14th June – 03:00 to 05:00 PM
  • 29th June – 05:00 to 07:00 PM
  • 4th July – 05:00 to 07:00 PM
  • 20th July – 03:00 to 05:00 PM
  • 3rd Aug – 05:00 to 07:00 PM
  • 16th Aug – To be decided

Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.
2. At the end of the program, a final assessment will be conducted.

Syllabus

Welcome to Machine Learning

  • Introduction To Machine Learning
  • Concept of Machine Learning – What/When/Why
  • Evolution of ML
  • Find out where Machine Learning is applied in Technology and Science. Applications in real scenarios.

Basics of Python

  • Basic programming syntax of Python, File Handling

Fundamental of Machine Learning Categories

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Working with Data

  • Reading Files, Scraping the Web, Cleaning, Munging and Manipulating

Visualizing Data

  • Bar Charts, Line Charts, Scatterplots

Machine Learning Python Packages

  • Data Analysis Packages
  • NumPy
  • Matplotlib
  • Pandas
  • Sklearn

Supervised Machine Learning

  • Regression
  • Classification
  • Generalization, Overfitting and Underfitting

UnSupervised Machine Learning

  • Challenges in unsupervised learning

Supervised Machine Learning Algorithms

  • Linear models
  • k-Nearest Neighbor
  • Naive Bayes Classifiers
  • Decision trees

Supervise ML – Classification

  • Classification – LogisticRegression, Sigmoid Function

Supervised ML – Regression

  • Understand how continuous supervised learning is different from discrete learning.
  • Code a Linear Regression in Python with scikit-learn.

UnSupervise ML – Clustering

  • k-Means clustering

Prepare Your Data For Machine Learning

  • Need For Data Pre-processing
  • Data Transforms
  • Rescale Data
  • Standardize Data
  • Normalize Data
  • Binarize Data

Evaluate the Performance of Machine Learning Algorithms with Resampling

  • Evaluate Machine Learning Algorithms
  • Split into Train and Test Sets
  • K-fold Cross Validation
  • Leave One Out Cross Validation
  • Repeated Random Test-Train Splits
  • Understand different error metrics such as MSE and MAE in the context of Machine Learning.
  • Which Techniques to Use When

Industrial use case walk through

  • Industrial use case walk through for supervised learning
  • Industrial use case walk through for unsupervised learning

Prerequisite

Basic Knowledge of Python Programming .

General Information
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