Machine learning has the predictive power and gain popularity in todays technology world. Data Scientist or Machine learning enthusiast looks for enhancing their career in the world of machine learning, you are at a right place. It’s a must-have skill for all aspiring data scientists and data analysts or anyone who wants to grapple all that raw data into refined trends and predictions.
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List of Top Free Machine Learning Courses
This course is provided by the Stanford university and it is accessible on coursera platform. this is 11 weeks long course and can be completed in approx. 61 hours. it is 100% online course and provides you a sharable certification upon course completion. it is self paced course, you can learn at your own pace from anywhere on any time.
Develop your skills by learning logistic regression, artificial neural network, machine learning algorithms and machine learning. learners interested in machine
learning can enroll this course and get full access to the course content. learners looking for certification need to apply for financial aid and gain your certificate.
Course Duration : 10 to 11 weeks long
Time Duration : Approx. 61 hours
Course Type : Self-paced
Subject : Data Science
Level : Introductory
Language : English
Video Transcript : English
Platform : coursera
Course Cost : FREE
Prerequisites : No Prior Experience
What you will Learn :
- Introduction to Machine learning
- Linear Regression with one variable
- Linear Algebra Review
- Linear Regression with multiple variables
- Octave/Matlab Tutorial
- Logistic Regression and Regularization
- Neural Networks and Support vector machines
- Advice for applying machine learning
- Machine learning system design
- Unsupervised learning & Dimensionality Reduction
- Anomaly Detection & Recommender Systems
- Large Scale Machine Learning
- Application Example:Photo OCR
Learn the fundamentals of machine learning by taking this course from UC San Diego. this course is available on edx platform. if you are looking for best free machine learning courses online then this is the right place to start your learning.
learn about machine learning’s role in data driven modeling, decision making and prediction. this course is available on two session starts in Jul and Aug every year. this is self paced course with no declines. learn about variety of supervised and unsupervised algorithms with theory. you will use real-world case studies in this course.
Course Duration : 10 weeks long
Time Duration : 8-10 hours/week
Course Type : Self-paced
Subject : Data Analysis & Statistics
Level : Advanced
Language : English
Video Transcript : English
Platform : edx
Course Cost : FREE
Prerequisites :
- Knowledge of Multivariate calculus & Linear algebra
- Probability & Statistics in data science using python
- Python for data science
What you will Learn :
- Linear models and extensions using kernel methods
- Classification, regression and conditional probability
- Representation learning and Ensemble methods
- Generative and discriminative models
This course is designed for beginner python developers who want to get started with machine learning. this course takes you through basic to implementation of machine learning. this course provides hands on experience with python and learn supervised and unsupervised learning. this course divided in three sections which contains 7 lectures. if you are a beginner and interested to learn machine learning then this course is the right choice to getting start.
Course Duration : Within week
Time Duration : 40 total mins
Course Type : Self-paced
Subject : Data Analysis & Statistics
Level : Beginner
Language : English
Video Transcript : English
Platform : Udemy
Course Cost : FREE
Prerequisites :
- Python and Matplotlib
- Pandas and Numpy
What you will Learn :
- Machine Learning Basic Concepts
- Machine Learning Implementation
If you are Machine learning aspirant and experienced data scientist who is looking for best course to learn machine learning, this course from udacity helps you to start your journey in machine learning through fun videos. this is self paced course which includes interactive quizzes and final project.
learn machine learning through rich learning content taught by industry experts. learn end to end process of investigating data and how to identify & extract useful features which best represent your data. learn how to evaluate the performance of machine learning algorithms. learn by doing exercises, instructor videos and use cases tackling with real world problems like self driving cars.
Course Duration : Approx. 10 Weeks
Time Duration : 65 to 67 hours
Course Type : Self-paced
Subject : Data Analysis & Statistics
Level : Intermediate
Language : English
Video Transcript : English
Platform : Udacity
Course Cost : FREE
Prerequisites :
- Must be proficient at programming in python & basic statistics
- Intro to Python Programming and Data Science
- Inferential Statistics and Descriptive Statistics
What you will Learn :
- What is Machine Learning
- Where Machine Learning is applied
- How to use Naive Bayes with scikit in python
- How to choose right kernel for your SVM
- How to code decision tree in python
- How to choose right machine learning algorithm
- How to look for patterns in email dataset
- Understand about different error metrics
- How to remove outliers to improve the quality of linear regression predictions
- How to find the center of clusters
- How to preprocess data with feature scaling
Start your machine learning journey by taking this beginner level course with alison. this is introductory level course which contains three module and 12 topics of machine learning. this is self paced course, you can complete at your own speed.
learn about unsupervised and supervised learning, linear regression and regularization, differnt types of regression, use of excel & matlab for simple and multiple regression, K-NN approach in data analytics with course assessment.
Course Duration : One Weeks
Time Duration : 1.5 – 3 hours
Course Type : Self-paced
Subject : Data Analysis
Level : Beginner
Language : English
Platform : Alison
Course Cost : FREE
Prerequisites :
- programming in python & basic statistics
- Intro to Python Programming and Data Science
What you will Learn :
- Difference between supervised, unsupervised and reinforced learning
- Explain what is linear regression
- Distinguish supervised and unsupervised data
- Describe when can be regularization used
- How to use Excel to perform multiple regression
- Distinguish R-squared & adjustment R squared
- Explain subset selection & when to use K-NN approach
