Machine Learning Course Online - Basic to Advanced

12 Lesson

36 Hours

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Enroll in our Machine Learning training program to learn how to build intelligent systems and predictive models. You’ll also learn basic, intermediate, and advanced techniques in supervised, unsupervised, reinforcement learning, neural networks, and natural language processing (NLP). This Machine Learning training course features advanced study materials, video tutorials, and practice sessions. Our expert trainers will teach you how to implement algorithms and models in real-time using popular ML tools and libraries. The online Machine Learning course comes in multiple modes like self-paced, one-on-one sessions, and live training, which you can choose as per your convenience. Our trainers take care of every aspirant and also help them prepare for industry-recognized certifications and real-world project applications.

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Machine Learning Course Overview

Machine Learning is one of the most in-demand technologies used in data science, artificial intelligence, and intelligent automation. igmGuru’s Machine Learning course offers hands-on, practical training designed by industry experts who have more than 20 years of experience. Our ML course helps you understand how systems learn from data to make predictions and automate tasks. Learn practical skills in deep learning, NLP, MLOps, and AI model deployment. If you are a beginner or looking to strengthen your ML skills, you will work on real-world datasets and use cases in a structured learning environment. Start your Machine Learning journey today and build the skills required to grow your career in data-driven and AI-focused roles.

Prerequisites

Basic understanding of math (algebra, statistics, probability)
Familiarity with Python programming
Basic knowledge of data handling (optional but helpful)
No prior machine learning experience required

What Will You Learn

Introduction to Machine Learning
Supervised Learning
Unsupervised Learning
Engineering and Data Preprocessing Features
Model Evaluation
Hyperparameter Tuning
Ensemble Techniques like XGBoost, LightGBM, CatBoost
Deep Learning Fundamentals
Transformers and Attention Mechanism
Natural Language Processing (NLP) with Transformers
Time Series Forecasting with ARIMA, Prophet, LSTM
Reinforcement Learning
Generative AI: GANs and Diffusion Models
Retrieval-Augmented Generation (RAG)
Prompt Engineering Fundamentals
Fine-Tuning Large Language Models (LLMs)
AI Agents and Agentic AI Fundamentals
Vector Databases Basics
Small Language Models (SLMs)
Transfer Learning for Computer Vision and NLP
AI Model Deployment
Pipelines and Monitoring in MLOps
Ethical Artificial Intelligence (AI)

20+ Industry Tools You'll Actually Use

Python
R
Jupyter Notebook
Anaconda
Scikit-learn
TensorFlow 2.x
Keras
PyTorch
Pandas
NumPy
Matplotlib
Seaborn
Google Colab
Hugging Face Transformers
FastAI
MLflow
Streamlit
OpenCV
Weights and Biases (W&B)
AutoML (Google AutoML, H2O.ai)
ONNX (Open Neural Network Exchange)

Is This Course Right for You?

This training is designed for a wide range of professionals. See if you recognise yourself.

Software Developers
Data Analysts
AI Enthusiasts
IT Professionals and Engineers
Students and Graduates in CS, Math, or Statistics
Business Professionals
Professionals implementing ML in projects
Anyone aiming for a career in Machine Learning

Job Roles After Machine Learning Course

Machine Learning Engineer
Data Scientist
AI Engineer
Data Analyst
MLOps Engineer

Benefits of Machine Learning Certification Course Online

Build strong foundations in Machine Learning and AI
Gain hands-on experience with real-world ML projects
Learn in-demand skills like Deep Learning, NLP, and MLOps
Improve problem-solving and data-driven decision-making skills
Prepare for high-demand job roles such as Machine Learning Engineer and MLOps Engineer
Higher salary growth and better career opportunities in AI-driven industries

Skills That Show Up in Every ML Job Description

Machine Learning Algorithms
Supervised Learning
Unsupervised Learning
Deep Learning
Neural Networks
Natural Language Processing
Computer Vision
Model Evaluation
Feature Engineering
Data Preprocessing
Prediction Models
Reinforcement Learning
Recommendation Systems
Model Deployment

ML Online Training Features

Instructor Led Training : 36 Hrs
Experienced & Professional Trainers
Small Batches Upto 10 Participants
24 X 7 Lifetime Support & Access
1 On 1 Training Option Available
Flexible Schedule

Machine Learning Training Modules

Lesson 1 - Introduction to Machine Learning

1. What is Machine Learning?

2. Types of ML: Supervised, Unsupervised, Reinforcement Learning

3. Applications and Use Cases Across Industries

Lesson 2 - Python for Machine Learning

1. Python Basics for ML (variables, loops, functions)

2. Libraries: NumPy, Pandas, Matplotlib, Seaborn

3. Data Manipulation and Visualization

Lesson 3 - Data Preprocessing & Feature Engineering

1. Handling Missing Data

2. Encoding Categorical Variables

3. Feature Scaling (Normalization & Standardization)

4. Feature Selection Techniques

Lesson 4 - Exploratory Data Analysis (EDA)

1. Understanding Data Distributions

2. Correlation and Outlier Detection

3. Data Visualization Techniques

Lesson 5 - Supervised Learning Algorithms

1. Linear Regression

2. Logistic Regression

3. Decision Trees and Random Forests

4. K-Nearest Neighbors (KNN)

5. Support Vector Machines (SVM)

Lesson 6 - Unsupervised Learning Algorithms

1. K-Means Clustering

2. Hierarchical Clustering

3. Principal Component Analysis (PCA)

Lesson 7 - Model Evaluation and Validation

1. Train-Test Split and Cross-Validation

2. Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC

3. Bias-Variance Tradeoff

Lesson 8 - Model Tuning & Optimization

1. Hyperparameter Tuning: Grid Search and Random Search

2. Overfitting vs Underfitting

3. Regularization Techniques (L1, L2)

Lesson 9 - Introduction to Deep Learning

1. Neural Networks Basics

2. Activation Functions and Layers

3. Forward and Backpropagation

4. Introduction to TensorFlow/Keras

Lesson 10 - Natural Language Processing (NLP)

1. Text Preprocessing (Tokenization, Stop Words, Lemmatization)

2. Bag of Words and TF-IDF

3. Sentiment Analysis and Text Classification

Lesson 11 - Time Series Analysis

1. Time Series Concepts

2. Forecasting Models (ARIMA, Prophet)

3. Trend, Seasonality, and Noise

Lesson 12 - Capstone Project

1. Real-world End-to-End ML Project

2. Problem Definition, Data Collection, EDA, Model Building, Evaluation

3. Model Deployment Basics

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Machine Learning Online Course Fees

1 ON 1 Training

US $ 1,299.00

100% Money Back Guarantee

Duration : 36 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 19 Jun 2026
Weekday Batch 22 Jun 2026
Weekend Batch 20 Jun 2026

Corporate Training

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

Trusted By Top Companies Worldwide

Want to know Today's Offer

Machine Learning Certification Exam

Machine Learning certifications help validate your skills in building and deploying ML models. Top certifications like Google Cloud's ML Engineer, AWS ML Specialty, IBM's ML Certificate, and Microsoft's Azure AI Fundamentals are highly valued in the industry. These credentials boost your career by proving your expertise in ML concepts and tools. While not mandatory, they add strong value to your resume. At igmGuru, we help you prepare with expert guidance and practical training.

Machine Learning Course Online FAQ

What exactly is machine learning, and how is it different from AI?

Machine learning is a subset of artificial intelligence. While AI is the broad goal of making machines behave intelligently, machine learning is the specific technique that gets us there - by training algorithms to find patterns in data, rather than hand-coding rules. Think of AI as the destination and ML as one of the main roads.

Is machine learning difficult?

Machine learning can be difficult to understand at first, specifically if you are unfamiliar with programming or mathematical concepts such as linear algebra, probability, and calculus. But with constant practice and availability to easy for beginners tools like as Python libraries (e.g., TensorFlow, Scikit-learn), it is feasible. To properly master a subject, our course can prove to be very useful.

What are the 4 basics of machine learning?

There are four key basics of Machine Learning namely supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. You will learn everything about these basics and much more in igmGuru’s Machine Learning Training online.

Can I learn ML in 1 month?

As per a general consensus, most ML experts say that it can take 6-9 months to learn ML. Of course, it does not happen all at once and you learn something new everyday. However, to become a true expert, you have to practice for a long duration, and learn continuously.

Which course is best for machine learning?

The best course for Machine Learning is being offered by igmGuru. This course covers all the key concepts of machine learning and is being taught by industry experts with 10+ years of experience in the field.