Applied Machine Learning Certification Training

SKU: 3825
10 Lesson
|
30 Hours
igmGuru's Applied Machine Learning training teaches you how to build, train, and deploy real-world ML models using Python. From regression and classification to deep learning and model deployment, this hands-on program prepares working professionals to solve practical business problems with confidence and industry-relevant skills.

Overview

This Applied Machine Learning course by igmGuru moves beyond pure theory to focus on practical implementation. In this Applied Machine Learning online course program, you will work with real datasets, popular Python libraries, and end-to-end project pipelines covering supervised learning, unsupervised learning, feature engineering, model evaluation, and deployment, building skills that map directly to today's data-driven job roles.

Prerequisites

A basic understanding of Python programming, high-school-level statistics, and elementary linear algebra is recommended before enrolling. Prior exposure to data analysis tools such as Excel or SQL is helpful but not mandatory, as igmGuru covers foundational concepts during the early sessions for learners coming from a non-technical background.

Course Objectives

  • Build a strong foundation in machine learning concepts, terminology, and workflow
  • Apply supervised and unsupervised learning algorithms to real business datasets
  • Learn data preprocessing, feature engineering, and feature selection techniques
  • Understand model evaluation metrics and techniques to avoid overfitting
  • Gain hands-on exposure to deep learning fundamentals using neural networks
  • Develop the ability to choose, tune, and deploy the right ML model for a use case

What You Will Learn

In this Applied Machine Learning online training program, you will learn the following skills that will help you to build a strong skill set.

  • Python for machine learning, including NumPy, Pandas, and Matplotlib
  • Data cleaning, transformation, and exploratory data analysis (EDA)
  • Regression techniques: linear, polynomial, and regularized models
  • Classification algorithms: logistic regression, decision trees, random forest, SVM
  • Unsupervised learning: K-means clustering, hierarchical clustering, PCA
  • Model evaluation metrics: accuracy, precision, recall, F1-score, ROC-AUC
  • Ensemble learning techniques such as bagging, boosting, and stacking
  • Introduction to neural networks and deep learning with TensorFlow/Keras
  • Hyperparameter tuning, cross-validation, and model optimization
  • Deploying machine learning models for real-world business applications

Who Is This Course For?

This course is designed for professionals and learners who want to apply machine learning to real business problems.

  • Data analysts and BI professionals moving into machine learning roles
  • Software developers and engineers exploring AI/ML career paths
  • Statisticians and researchers wanting hands-on ML implementation skills
  • Graduates and final-year students preparing for data science careers
  • Product managers and business analysts who work closely with ML teams
  • IT professionals looking to upskill in artificial intelligence and ML

Tools and Technologies Covered

In this program, you will work with the following tools and frameworks.

  • Python
  • NumPy and Pandas
  • Matplotlib and Seaborn
  • Scikit-learn
  • TensorFlow and Keras
  • Jupyter Notebook / Google Colab
  • Git and GitHub (version control basics)

Key Features

Course Curriculum

1. What is Machine Learning and how it differs from traditional programming
2. Types of ML: supervised, unsupervised, and reinforcement learning
3. The end-to-end machine learning workflow
1. Python refresher for data science
2. Working with NumPy and Pandas
3. Data visualization using Matplotlib and Seaborn
1. Handling missing values and outliers
2. Feature scaling, encoding, and transformation
3. Exploratory data analysis techniques
1. Linear and polynomial regression
2. Regularization: Ridge and Lasso
3. Model evaluation for regression problems
1. Logistic regression and decision trees
2. Random forest and support vector machines
3. Confusion matrix, precision, recall, and ROC-AUC
1. K-means and hierarchical clustering
2. Dimensionality reduction with PCA
3. Anomaly detection basics
1. Bagging and boosting techniques
2. Random forest and gradient boosting (XGBoost)
3. Model stacking strategies
1. Neural network fundamentals
2. Building models with TensorFlow and Keras
3. Activation functions and optimizers
1. Cross-validation and hyperparameter tuning
2. Avoiding overfitting and underfitting
3. Deploying ML models for real-world use
1. End-to-end applied ML project on a real business dataset
2. Project presentation and peer review
3. Career guidance and interview preparation
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Course Fees

Online Class Room Program

US $ 799.00
100% Money Back Guarantee
  • Duration : 30 Hrs
  • Plus Self Paced

Classes Starting From

  • Fast Track Batch 19 Jul 2026
  • Weekday Batch 20 Jul 2026
  • Weekend Batch 25 Jul 2026

1 ON 1 Training

US $ 899.00
100% Money Back Guarantee
  • Duration : 30 Hrs
  • Plus Self Paced

Classes Starting From

  • Fast Track Batch 19 Jul 2026
  • Weekday Batch 20 Jul 2026
  • Weekend Batch 25 Jul 2026

Corporate Training

Corporate Training
  • Customized Training Delivery Model
  • Flexible Training Schedule Options
  • Industry Experienced Trainers
  • 24x7 Support

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Want to know Today's Offer

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Applied Machine Learning Certification

On completing the course, igmGuru provides a certificate validating your hands-on skills in building, evaluating, and deploying machine learning models with Python. It's earned through hands-on assignments and a capstone project, reflecting real applied capability rather than mere attendance. Recognized by employers, this certification strengthens your resume for ML and data science roles and is backed by igmGuru's interview preparation and resume support to help you convert it into job opportunities.

Applied Machine Learning Certification

FAQ's

Basic familiarity with Python is helpful but not mandatory. igmGuru covers Python fundamentals early in the course so learners from non-technical backgrounds can catch up comfortably.

Yes. The course includes multiple hands-on assignments and a capstone project based on real-world datasets, so you apply concepts as you learn them rather than just studying theory.

Yes, the course is designed with flexible weekday and weekend live batches, along with lifetime access to recordings, making it convenient for full-time professionals to learn at their own pace.

You will receive an igmGuru course completion certificate that validates your applied machine learning skills and can be added to your resume or LinkedIn profile.

Yes, the course includes an introduction to neural networks and deep learning using TensorFlow and Keras, in addition to core machine learning algorithms.

igmGuru offers placement support including resume building and interview preparation guidance to help learners transition into ML-related roles.

The course structure prioritizes practical implementation, real datasets, and end-to-end projects over heavy mathematical derivations, helping you become job-ready faster.

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