Adoption of modern technologies including machine learning and artificial intelligence have helped a number of businesses to excel and be more competitive in the marketplace.
Businesses like healthcare, e-commerce, automotive, robotics, financial services, transport, oil and gas, among others have been explicitly using machine learning algorithms to ensure better decision making and meet business objectives.
From prototyping to developing a product, the usage of machine learning has proved to be an incredibly powerful tool.
Considering the skill gap in this highly growing domain, it can be a wise move to pick up a machine learning course that can help you have a highly rewarding career in the field of machine learning.
We have handpicked some of the best-rated machine learning courses from the best learning platforms to help you decide on a course that can be suitable for your professional growth.
Criteria on which the courses have been chosen -
- Focus on machine learning
- Highly rated by the course seekers/industry – 4.5 stars and above
- Use free, open-source programming languages and free, open-source libraries
- Include case studies and programming projects for hands-on experience
- Focus on the algorithms functioning
- Be self-paced, on-demand or available every month or so
- Engaging and personable instructors
Course details
- Rating – 4.9 Stars
- Duration – 54 Hours
- Skill Level – Beginner
Course description
Machine Learning by Stanford University is one of the pioneer courses on the topic.
This is taught by Andrew Ng and focussed on theoretical aspects of Machine learning.
It introduces the learner to machine learning, datamining, and statistical pattern recognition.
It covers topics like supervised learning, unsupervised learning, best practices in machine learning, among others.
The course will also draw from numerous case studies and applications, so that you can to apply learning algorithms to building smart robots, computer vision, medical informatics, audio, database mining, and others
Course content
Week 1
- Introduction to Machine Learning
- Linear Regression with One Variable
- Linear Algebra Review
Week 2 - Linear Regression with Multiple Variables
- Octave/Matlab Tutorial
Week 3 - Logistic Regression
- Regularization
Week 4 - Neural Networks: Representation
Week 5 - Neural Networks: Learning
Week 6 - Advice for Applying Machine Learning
- Machine Learning System Design
Week 7 - Support Vector Machines
Week 8 - Unsupervised Learning
- Dimensionality Reduction
Week 9 - Anomaly Detection
- Recommender Systems
Week 10 - Large Scale Machine Learning
- Application Example: Photo OCR
Course details
- Rating – 4.9 Stars
- Duration – 54 Hours
- Skill Level – Beginner
Course description
This course dives into the basics of machine learning using Python.
In this course, you will explore -
The purpose of Machine Learning and its application in the real world.
Get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
Course content
Week 1
Introduction to Machine Learning
Week 2
Regression
Week 3
Classification
Week 4
Clustering
Week 5
Recommender Systems
Week 6
Final Project
Course details
- Rating – 4.6 Stars
- Duration – 18 Hours
- Skill Level – Beginner
Course description
With this course, you will learn how to Learn how to use the R programming language for data science and machine learning and data visualization.
The course comprises of over 100 HD video lectures and detailed code notebooks for every lecture.
You will learn to program with R, create amazing data visualizations, and use Machine Learning with R.
Course content
- Programming with R
- Advanced R Features
- Using R Data Frames to solve complex tasks
- Use R to handle Excel Files
- Web scraping with R
- Connect R to SQL
- Use ggplot2 for data visualizations
- Use plotly for interactive visualizations
- Machine Learning with R, including:
- Linear Regression
- K Nearest Neighbors
- K Means Clustering
- Decision Trees
- Random Forests
- Data Mining Twitter
- Neural Nets and Deep Learning
- Support Vectore Machines
Course details
- Duration - 5 Weeks
- Skill Level – Beginner
Course description
This data science course will help you to understand the principles of machine learning and derive practical solutions using predictive analytics.
The course will further explore why algorithms play an essential role in Big Data analysis.
In this program, you will learn how machine learning uses computer algorithms to search for patterns in data and understand how to use data patterns to make decisions and predictions with real-world examples.
Course details
- Rating – 4.7 Stars
- Duration – 13 Hours
- Skill Level – Beginner
Course description
The course investigates design strategies that businesses, employees and designers can adopt to find new opportunity in such a rapidly changing professional landscape.
You will learn to about the integration of design approaches in business practices, analyse important skills and attributes that designers, employees, and businesses need to be successful in a speculative, technologically enhanced future
Course content
Week 1
What is the Future of Work?
Week 2
The Importance of Being Human in a World of Automation
Week 3
Designing the Future of Work
Week 4
Industry and Academic Expert Video Profiles
Course details
- Rating – 4.5 Stars
- Duration – 7 Months
- Skill Level – Intermediate
Course description
This is a comprehensive data science and machine learning course that even people with no programming experience can take.
It includes over 35 hours of HD video tutorials and builds your programming knowledge while solving real-world problems.
Course content
- Data cleaning and pre-processing
- Data exploration and visualisation
- Linear regression
- Multivariable regression
- Optimisation algorithms and gradient descent
- Naive bayes classification
- Descriptive statistics and probability theory
- Neural networks and deep learning
- Model evaluation and analysis
- Serving a tensorflow model
Course details
- Rating – 4.7 Stars
- Duration – 7 Months
- Skill Level – Intermediate
Course description
In this specialization course, you will learn from the leading Machine Learning researchers at the University of Washington.
The course includes a number of practical case studies to help you gain applied experience in major areas of Machine Learning including prediction, classification, clustering, and information retrieval.
At the end of this course, you will be able to analyze large and complex datasets, create better systems, and build intelligent applications that can make predictions from data.
Course content
Course 1 - Machine Learning Foundations: A Case Study Approach
Course 2 - Machine Learning: Regression
Course 3 - Machine Learning: Classification
Course 4 - Machine Learning: Clustering & Retrieval
Course details
- Rating – 4.6 Stars
- Duration – 7 Hours
- Skill level – Advanced
Course description
The program is designed for existing data science practitioners with expertise in building machine learning models.
It aims to sharpen the skills on building and deploying AI in large enterprises through different lectures and case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines.
Course content
Week 1
Model Evaluation and Performance Metrics
Week 2
Building Machine Learning and Deep Learning Models
Course Details
- Rating – 4.6 Stars
- Duration – 7 Hours
- Skill Level – Advanced
Course description
This course offers an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python.
With the help of these models, you can derive smart analytics tools and use it for various purposes including image classification, text and sentiment analysis, among others.
The course contains two case studies - forecasting customer behaviour after a marketing campaign, and flight delay and cancellation predictions.
Course content
Week 1
Decision trees
Week 2
Random forests and support vector machines
Week 3
Support vector machines
Week 4
Neural networks
Week 5
Neural network estimation and pitfalls
Week 6
Model comparison
Course details
- Duration – 12 months
Course description
The course introduces you to the intricacies of data science and machine learning techniques.
You will get a hands on experience of popular and in-demand tools in the BDA and ML area so that you can develop practical problem solving skills.
The program also includes an extensive capstone project.
Besides, you would get a chance to attend two campus immersion modules of 3 days at IIITA, Prayagraj, at the commencement and culmination of the program.
Eligibility
- For Indian Participants – Graduates (10+2+3) or Diploma Holders (only 10+2+3) from a recognized university (UGC/AICTE/DEC/AIU/State Government) in any discipline.
- For International Participants – Graduation or equivalent degree from any recognized University or Institution in their respective country.
- 4 years of work experience.
Prerequisites
- Mathematics or Statistics as a subject in Class XII or Graduation.
- Formal education in or knowledge/experience of at least one programming language.
This course is entirely hands-on and so it is recommended, though not a necessity that students have two devices (laptop/desktop) – one to follow the lecture, and the other for hands-on practice alongside during the class.
Course content
- Fundamentals of python
- Data wrangling
- Statistics and probability
- Machine learning models in Python
- Data visualization using MATPLOTLIB
- Deep learning using Tensorflow
- Handling big data with Spark
- Capstone project
- On campus component
All the best!
