Online Course
Machine Learning - Basic Level Course

Course Overview

This course is the perfect place for beginners to understand the core idea of building systems that have the ability to automatically learn from data and improve the experience without being explicitly programmed. In this course, you will learn about concepts of Machine Learning, effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

The course will be self-paced, designed, and mentored by Industry experts having hands-on experience in ML-based industry projects. It will be an online class, so you can attend this class from any geographical location. It will be a self-paced course so you can cover the material at any time you wish to.

If you are looking for the advanced Live course of Machine Learning, here's the course for you:

Should know the basics of any one programming language: C / C++, Java or Python
Recommended for:
Anyone who wants to learn and build ML-based solutions specifically

  • College students who are looking for training in Machine Learning
  • Working Professionals who want to learn machine learning concepts.

Course Features

  • Training Certificate
  • Internship Opportunities at GeeksforGeeks
  • Project-based learning which will add stars to your resume 
  • Understanding of how data scientists approach to problems

What You Will Learn

  • Python Tools related to Data Science
  • Data Handling
  • Through Mathematical understanding of Machine Learning
  • Application of Machine Learning Models to solve real-life problems

Course Content

  • What is Machine Learning

  • Data in Machine Learning

  • Installation of Anaconda

  • Working of Jupyter Notebook

  • Numpy
    • Numpy - Creating Numpy Array
    • Numpy - Array Dimensions
    • Numpy - Reversing Rows and Columns
    • Numpy - Specific Element Extraction
    • Numpy - Basic Statistics
    • Numpy - Reshaping and Flattening
    • Numpy - Random Arrays and Sequences
    • Numpy - Unique Items and Count
  • Pandas
    • Pandas - Working of DataFrames
    • Pandas - Working on CSV
    • Pandas - Missing Values
    • Pandas - Statistics 
  • Matplotlib
    • Matplotlib - Line Graph and Scatter Plot
    • Matplotlib - Bar Graph
    • Matplotlib - Bubble Graph and Pie Chart
  • Categorical data
  • Data Scaling Intuition
  • Data Scaling
  • Data Splitting Intuition
  • Data Splitting 
  • Handling Missing Data
  • Linear Regression
    • Linear Regression Intuition - 1
    • Linear Regression Intuition - 2
    • Linear Regression Scratch - Part 1
    • Linear Regression Scratch - Part 2 - Forward Propagation
    • Linear Regression Scratch - Part 3 - Cost Function
    • Linear Regression Scratch - Part 4 - Gradient Descent
    • Linear Regression Scratch - Part 5 - Training Linear Regression Model and Predicting 
    • Linear Regression using sklearn
  • Polynomial Linear Regression
    • Polynomial Linear Regression Intuition 
    • Polynomial Linear Regression Hands-On
  • Support Vector
    • Support Vector Regressor Intuition
    • Support Vector 2 Kernels
    • Support Vector Regression Code
  • Decision Tree
    • Decision Tree Intuition
    • Decision Tree Code
  • Random Forest
    • Random Forest Intuition
    • Random Forest Code
  • Logistic Regression
    • Logistic Regression Intuition
    • Logistic Regression Code
  • K-Nearest Neighbor
    • K-Nearest Neighbor Intuition
    • K-Nearest Neighbor Code
  • Naive Bayes
    • Naive Bayes Intuition
    • Naive Bayes Code
  • Decision Tree
    • Decision Tree Intuition
    • Decision Tree Code
  • Random Forest
  • K-Means Algorithm
    • K-Means Intuition
    • K-Means Elbow Method
    • K-Means Code
  • Agglomerative Algorithm
    •  Agglomerative Intuition
    • Agglomerative Dendrogram
    • Agglomerative Code
  • Feature Selection
    • Feature Selection - Correlation Matrix
    • Feature Selection - ExtraTreeClassifier
    • CHI Square Test
    • Feature Selection - KBest Method
  • K-Fold 
    • K-Fold Intuition
    • K-Fold Code
  • Principal Component Analysis (PCA)
  • t-distributed Stochastic Neighbor Embedding (TSNE)
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  1. Is there any number to contact for any query?
    You may call us on our toll-free number: 1800 258 4458  or Drop us an email at

  2. Is this a certification course?
    Yes, It's a GeeksforGeeks certified program that includes projects along with learning. All students will receive a Training certificate with appropriate grades.
  3. How can I register for the course?
    Click on the Signup for free button & Pay Fees online
  4. Is there any demo lecture video of this course?
    Yes, you may access the demo lecture here: Demo Video for Machine Learning Course

Course Registration

Batch Date Type Register
ML Foundation Active Online Classes