Machine Learning Training Course


Our Machine Learning certification training course online includes ✔️ML skills ✔️Math Refresher ✔️4 Projects to become a Machine Learning Engineer. Enroll now!

SKU: ee88c51c220a Category:

Program Overview:
This online course offers an in-depth overview of machine learning topics, including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling. You will also learn how to use Python to draw predictions from data.

Program Features:

  • 14 hours of Online self-paced learning
  • Four industry-based course-end projects
  • Interactive learning with Jupyter notebooks integrated labs
  • Dedicated mentoring session from faculty of industry experts

Delivery Mode:
Online self-paced learning

This course requires an understanding of:

  • Statistics
  • Mathematicss
  • Python programming

Knowledge of these fundamental courses:

  • Python for Data Science
  • Math Refresher
  • Statistics for Data Science

Target Audience:

  • Data analysts looking to upskil
  • Data scientists engaged in prediction modeling
  • Any professional with Python knowledge and interest in statistics and math
  • Business intelligence developers

Key Learning Outcomes:

  • Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling
  • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
  • Acquire thorough knowledge of the statistical and heuristic aspects of machine learning
  • Business intelligence developers
  • Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
  • Validate machine learning models and decode various accuracy metrics.
  • Improve the final models using another set of optimization algorithms, which include boosting &and bagging techniques
  • Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning

Certification Details and Criteria:

  • 85 percent completion of online self-paced learning or attendance of one live virtual classroom
  • A score of at least 75 percent in the course-end assessment
  • Successful evaluation in at least one project

Course Curriculum:

Lesson 01 – Course Introduction

  • Course Introduction

Lesson 02 – Introduction to AI and Machine Learning

  • Learning Objectives
  • The emergence of Artificial Intelligence
  • Artificial Intelligence in Practice
  • Recommender Systems
  • Relationship Between Artificial Intelligence, Machine Learning, and Data Science – Part A
  • Relationship Between Artificial Intelligence, Machine Learning, and Data Science – Part B
  • Definition and Features of Machine Learning
  • Machine Learning Approaches
  • Machine Learning Techniques
  • Applications of Machine Learning – Part A
  • Applications of Machine Learning – Part B
  • Key Takeaways

Lesson 03 – Data Preprocessing

  • Learning Objectives
  • Data Exploration: Loading Files
  • Demo: Importing and Storing Data
  • Practice: Automobile Data Exploration I
  • Data Exploration Techniques: Part 1
  • Data Exploration Techniques: Part 2
  • Seaborn
  • Demo: Correlation Analysis
  • Practice: Automobile Data Exploration II
  • Data Wrangling
  • Missing Values in a Dataset
  • Outlier Values in a Dataset