Machine Learning Courses & Certifications Online
Machine learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. It focuses on the development of algorithms that can analyze and interpret data to make predictions or decisions.
Key Concepts of Machine Learning
Types of Machine Learning
- Supervised Learning: Involves training a model on labeled data, meaning the input data comes with corresponding output labels. Examples include classification and regression tasks.
- Unsupervised Learning: Deals with unlabeled data. The model tries to find patterns or intrinsic structures in the input data. Examples include clustering and association.
- Reinforcement Learning: Involves training a model to make sequences of decisions by rewarding or penalizing it based on its actions. It's commonly used in game playing and robotics.
Related: Different
Machine learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. It focuses on the development of algorithms that can analyze and interpret data to make predictions or decisions.
Key Concepts of Machine Learning
Types of Machine Learning
- Supervised Learning: Involves training a model on labeled data, meaning the input data comes with corresponding output labels. Examples include classification and regression tasks.
- Unsupervised Learning: Deals with unlabeled data. The model tries to find patterns or intrinsic structures in the input data. Examples include clustering and association.
- Reinforcement Learning: Involves training a model to make sequences of decisions by rewarding or penalizing it based on its actions. It's commonly used in game playing and robotics.
Related: Different Types of Machine Learning
Key Algorithms of Machine Learning
- Linear Regression: Used for predicting a continuous value.
- Logistic Regression: Used for binary classification problems.
- Decision Trees: Used for both classification and regression tasks.
- Support Vector Machines (SVM): Used for classification tasks.
- Neural Networks: Form the basis of deep learning, useful for complex pattern recognition tasks.
Related - Top 10 Machine Learning Algorithms
Related - Difference between Machine Learning and Deep Learning
Why Learn Machine Learning in 2024?
Learning machine learning in 2024 is an excellent decision for several reasons. The field continues to grow rapidly, offering numerous career opportunities and applications across various industries. Here are some key reasons to learn machine learning in 2024:
- High demand and growth: The demand for AI and machine learning specialists is projected to increase by 40% from 2023 to 2027. As data generation and the need for data-driven decision-making grow across industries, the job market for machine learning professionals is expanding rapidly.
- Lucrative salaries: Machine learning jobs are among the highest-paying in the tech industry. Roles like Data Scientist, AI Engineer, and Machine Learning Engineer command salaries ranging from INR 15-25 Lacs. The scarcity of machine learning talent makes it an attractive and rewarding career path.
- Intellectual challenge: Machine learning presents a dynamic landscape of complex problems to solve. The field requires constant learning and adaptation to new technologies and methodologies, making it an exciting area for problem-solvers and those passionate about pushing the boundaries of what machines can do.
- Broad applications: Machine learning has applications in virtually every industry, from healthcare and education to finance and marketing. Learning ML skills can open up opportunities across diverse sectors and enable you to make a significant impact.
- Automation and efficiency: ML enables businesses to automate tasks, improve operations, and gain valuable insights from data. Professionals with ML expertise are valuable assets in helping organizations leverage data to drive innovation and growth.
Related - How to Become a Machine Learning Engineer
What Topics Are Typically Covered in Machine Learning Courses?
| Category |
Topics |
Details |
| Introduction to Machine Learning |
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| Mathematics and Statistics for Machine Learning |
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| Data Preprocessing and Exploration |
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| Supervised Learning Algorithms |
|
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| Unsupervised Learning Algorithms |
|
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| Advanced Topics in Machine Learning |
|
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| Model Evaluation and Validation |
|
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| Practical Implementation |
|
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| Special Topics and Case Studies |
|
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Related - How to Become a Machine Learning Expert in 9 Months
Must Read - Top 10 Machine Learning Projects for Beginners
How To Choose The Best Machine Learning Courses For Your Career Goal?
Here’s a step-by-step guide to help you make an informed decision:
1. Assess Your Current Skill Level
- Beginner: If you are new to machine learning, look for courses that cover the basics and provide a solid foundation. Courses should start with fundamental concepts and gradually introduce more complex topics.
- Intermediate: If you have some experience with machine learning, choose courses that delve into advanced algorithms and applications.
- Advanced: For those with substantial knowledge, look for specialized courses or advanced topics like deep learning, reinforcement learning, or specific industry applications.
2. Define Your Career Goals
- Data Scientist: Look for courses that cover a broad range of machine learning algorithms, data preprocessing, and model evaluation techniques.
- AI Engineer: Focus on courses that include programming and implementation of machine learning models, particularly using frameworks like TensorFlow or PyTorch.
- Researcher: Opt for courses that emphasize theoretical concepts, mathematics, and cutting-edge research in machine learning.
3. Check Course Content and Structure
- Core Topics: Ensure the course covers essential machine learning topics like supervised and unsupervised learning, neural networks, and model evaluation.
- Hands-On Projects: Practical experience is crucial. Look for courses that include projects or labs where you can apply what you’ve learned.
- Programming Languages: Most machine learning courses use Python. Ensure you are comfortable with the programming language used in the course.
4. Evaluate the Instructor’s Credentials
- Expertise: Check the instructor’s background in machine learning and their professional experience.
- Teaching Style: Look for reviews or sample lectures to see if their teaching style matches your learning preference.
5. Consider Course Format and Flexibility
- Self-Paced vs. Scheduled: Decide if you prefer self-paced courses that allow you to learn at your own speed or scheduled courses with fixed deadlines.
- Interactive Elements: Courses with interactive elements like forums, Q&A sessions, and peer reviews can enhance your learning experience.
6. Read Reviews and Testimonials
- Student Feedback: Reviews from past students can provide insights into the course quality, content relevance, and instructor effectiveness.
- Course Ratings: Platforms like Coursera, edX, and Udemy often have ratings for their courses. Look for highly-rated courses with positive feedback.
7. Compare Costs and Certification
- Budget: Consider the cost of the course and whether it fits your budget. Some platforms offer financial aid or scholarships.
- Certification: If a certificate is important for your career goals, ensure the course offers a recognized certification upon completion.
Must Read: Difference Between AI & ML
Best Machine Learning Courses For You
| Course Name |
Platform |
Institution |
Instructor |
Duration |
Level |
| Machine Learning |
Coursera |
Stanford University |
Andrew Ng |
11 weeks |
Intermediate |
| Machine Learning A-Z™: Hands-On Python & R In Data Science |
Udemy |
- |
Kirill Eremenko, Hadelin de Ponteves |
41.5 hours |
All Levels |
| Deep Learning Specialization |
Coursera |
DeepLearning.AI |
Andrew Ng |
5 months |
Advanced |
| Python for Data Science and Machine Learning Bootcamp |
Udemy |
- |
Jose Portilla |
25 hours |
Beginner |
| Machine Learning for Data Science and Analytics |
edX |
Columbia University |
- |
12 weeks |
Introductory |
| Applied AI with DeepLearning |
Coursera |
IBM |
- |
5 months |
Intermediate |
| Machine Learning Specialization |
Coursera |
University of Washington |
- |
8 months |
Advanced |
| Introduction to TensorFlow for AI, Machine Learning, and Deep Learning |
Coursera |
DeepLearning.AI |
Andrew Ng |
4 weeks |
Beginner |
| Machine Learning with Python |
edX |
IBM |
- |
5 weeks |
Introductory |
| Advanced Machine Learning Specialization |
Coursera |
National Research University Higher School of Economics |
- |
7 months |
Advanced |
Must Explore - Top 10 Free Machine Learning Courses to Take Up in 2023
Machine Learning Courses on Coursera
| Course Name |
Institution |
Duration |
Level |
| Machine Learning |
Stanford University |
11 weeks |
Intermediate |
| Deep Learning Specialization |
DeepLearning.AI |
5 months |
Advanced |
| AI For Everyone |
DeepLearning.AI |
4 weeks |
Beginner |
| Machine Learning for Everyone |
University of London |
4 weeks |
Beginner |
| Applied AI with DeepLearning |
IBM |
5 months |
Intermediate |
| Machine Learning Specialization |
University of Washington |
8 months |
Advanced |
| Introduction to TensorFlow for Artificial Intelligence, Machine Learning... |
DeepLearning.AI |
4 weeks |
Beginner |
| Python and Machine Learning for Data Science |
IBM |
4 months |
Beginner |
| Advanced Machine Learning Specialization |
National Research University Higher School of Economics |
7 months |
Advanced |
| Structuring Machine Learning Projects |
DeepLearning.AI |
2 weeks |
Intermediate |
Explore - Coursera Machine Learning Courses
Machine Learning Courses on Udemy
| Course Name |
Instructor |
Duration |
Level |
| Machine Learning A-Z™: Hands-On Python & R In Data Science |
Kirill Eremenko, Hadelin de Ponteves |
41.5 hours |
All Levels |
| Python for Data Science and Machine Learning Bootcamp |
Jose Portilla |
25 hours |
Beginner |
| Machine Learning, Data Science and Deep Learning with Python |
Frank Kane |
15.5 hours |
Beginner |
| Deep Learning A-Z™: Hands-On Artificial Neural Networks |
Kirill Eremenko, Hadelin de Ponteves |
23.5 hours |
All Levels |
| Complete Machine Learning & Data Science Bootcamp 2023 |
365 Careers |
36.5 hours |
All Levels |
| Machine Learning with Python |
Jose Portilla |
21.5 hours |
Beginner |
| Advanced AI: Deep Reinforcement Learning in Python |
Lazy Programmer Inc. |
17 hours |
Advanced |
| The Data Science Course 2023: Complete Data Science Bootcamp |
365 Careers |
28 hours |
Beginner |
| Mathematics for Machine Learning |
SuperDataScience Team |
10.5 hours |
Beginner |
| Bayesian Machine Learning in Python: A/B Testing |
Lazy Programmer Inc. |
11.5 hours |
Intermediate |
Explore: Udemy Machine Learning Course
Machine Learning Courses on edX
| Course Name |
Institution |
Duration |
Level |
| Machine Learning with Python: from Linear Models to Deep Learning |
IBM |
5 weeks |
Introductory |
| Principles of Machine Learning |
Microsoft |
6 weeks |
Intermediate |
| Machine Learning for Data Science and Analytics |
Columbia University |
12 weeks |
Introductory |
| MicroMasters Program in Artificial Intelligence |
Columbia University |
10 months |
Advanced |
| Machine Learning |
University of Washington |
10 weeks |
Intermediate |
| Artificial Intelligence: Principles and Techniques |
Columbia University |
12 weeks |
Intermediate |
| Machine Learning for Everyone |
Microsoft |
6 weeks |
Introductory |
| Deep Learning |
IBM |
6 weeks |
Intermediate |
| Advanced Machine Learning |
Harvard University |
8 weeks |
Advanced |
| Machine Learning and Reinforcement Learning in Finance |
New York University |
8 weeks |
Intermediate |
Explore - edX Machine Learning Courses
Top Free Machine Learning Courses
| Course Name |
Platform |
Institution |
Instructor |
Duration |
Level |
| Machine Learning |
Coursera |
Stanford University |
Andrew Ng |
11 weeks |
Intermediate |
| Deep Learning Specialization |
Coursera |
DeepLearning.AI |
Andrew Ng |
5 months |
Advanced |
| AI For Everyone |
Coursera |
DeepLearning.AI |
Andrew Ng |
4 weeks |
Beginner |
| Machine Learning for Everyone |
Coursera |
University of London |
- |
4 weeks |
Beginner |
| Applied AI with DeepLearning |
Coursera |
IBM |
- |
5 months |
Intermediate |
| Machine Learning Specialization |
Coursera |
University of Washington |
- |
8 months |
Advanced |
| Introduction to TensorFlow for AI, Machine Learning, and Deep Learning |
Coursera |
DeepLearning.AI |
Andrew Ng |
4 weeks |
Beginner |
| Machine Learning with Python |
edX |
IBM |
- |
5 weeks |
Introductory |
| Advanced Machine Learning Specialization |
Coursera |
National Research University Higher School of Economics |
- |
7 months |
Advanced |
| Python and Machine Learning for Data Science |
Coursera |
IBM |
- |
4 months |
Beginner |
Must Check - Top 10 Free Machine Learning Courses
What Career Opportunities You Can Pursue with a Certification in Machine Learning?
There are a lot of machine learning jobs available that you can do once you complete your certification or degree in machine learning.
Let’s have a glimpse of some of the job opportunities.
| Job Title |
Job Description |
Skills Required |
Potential Employers |
Average Salary (INR) |
| Machine Learning Engineer |
Develop and implement ML models, algorithms, and solutions. |
Python, R, TensorFlow, PyTorch, data preprocessing, model deployment |
TCS, Infosys, Wipro, Accenture, HCL Technologies |
8-20 LPA |
| Data Scientist |
Analyze and interpret complex data, build predictive models, and provide data-driven insights. |
Statistics, Python, R, SQL, data visualization, machine learning |
IBM, Mu Sigma, Fractal Analytics, ZS Associates, Cognizant |
6-18 LPA |
| AI/ML Research Scientist |
Conduct research to advance the field of AI/ML and develop new algorithms and models. |
Deep learning, neural networks, research methodologies, programming |
Google Research, Microsoft Research, IBM Research, academic institutions |
10-25 LPA |
| Data Analyst |
Collect, process, and perform statistical analysis on data sets to provide business insights. |
SQL, Excel, Python, R, data visualization, statistical analysis |
Deloitte, KPMG, EY, PwC, Capgemini |
4-10 LPA |
| Business Intelligence (BI) Developer |
Design and develop BI solutions, including data warehouses, dashboards, and reports. |
SQL, data modeling, BI tools (Tableau, Power BI), Python |
TCS, Cognizant, Infosys, Wipro, Tech Mahindra |
5-12 LPA |
| Big Data Engineer |
Design, develop, and manage big data infrastructure and tools. |
Hadoop, Spark, Kafka, NoSQL databases, Python, Java |
Amazon, Flipkart, Paytm, Reliance Jio, Ola |
7-15 LPA |
| Natural Language Processing (NLP) Engineer |
Develop systems that can understand and interpret human language. |
NLP techniques, Python, TensorFlow, PyTorch, linguistics |
Google, Amazon, Microsoft, TCS, Infosys |
8-18 LPA |
| Computer Vision Engineer |
Develop algorithms and models to process and analyze visual data from images and videos. |
OpenCV, TensorFlow, PyTorch, Python, deep learning |
NVIDIA, Intel, Qualcomm, Samsung, startups in AI |
9-20 LPA |
| Robotics Engineer |
Design and develop robotic systems that can perform specific tasks using AI/ML. |
Robotics, Python, ROS, machine learning, control systems |
ISRO, DRDO, iRobot, GreyOrange, startups in robotics |
6-15 LPA |
| AI Product Manager |
Oversee the development and deployment of AI/ML products, bridging the gap between technical and business teams. |
Product management, machine learning, business strategy, communication skills |
Google, Microsoft, Amazon, startups in AI |
12-30 LPA |
Top Colleges Offering Bachelor’s Degrees in Machine Learning
| Program Name |
University |
Duration |
Degree Type |
| B.Tech in Computer Science and Engineering with specialization in Artificial Intelligence and Machine Learning |
Vellore Institute of Technology (VIT) |
4 years |
Bachelor's |
| B.Tech in Computer Science and Engineering with specialization in Artificial Intelligence and Machine Learning |
SRM Institute of Science and Technology |
4 years |
Bachelor's |
| B.Tech in Computer Science and Engineering with specialization in Data Science and Machine Learning |
Amity University |
4 years |
Bachelor's |
| B.Tech in Computer Science and Engineering with specialization in Artificial Intelligence and Machine Learning |
Manipal Academy of Higher Education (MAHE) |
4 years |
Bachelor's |
| B.Tech in Artificial Intelligence and Data Science |
Lovely Professional University (LPU) |
4 years |
Bachelor's |
| B.Tech in Artificial Intelligence and Machine Learning |
Chitkara University |
4 years |
Bachelor's |
| B.Tech in Computer Science and Engineering with specialization in Artificial Intelligence and Machine Learning |
Jain University |
4 years |
Bachelor's |
| B.Tech in Computer Science and Engineering with specialization in Artificial Intelligence and Machine Learning |
Chandigarh University |
4 years |
Bachelor's |
| B.Tech in Computer Science and Engineering with specialization in Artificial Intelligence and Machine Learning |
Reva University |
4 years |
Bachelor's |
| B.Tech in Computer Science and Engineering with specialization in Artificial Intelligence and Machine Learning |
Kalinga Institute of Industrial Technology (KIIT) |
4 years |
Bachelor's |
Top Colleges Offering Master’s Degrees in Machine Learning
| Program Name |
University |
Duration |
Degree Type |
| M.Tech in Machine Learning and Artificial Intelligence |
Indian Institute of Technology (IIT) Bombay |
2 years |
Master's |
| M.Tech in Artificial Intelligence and Machine Learning |
Indian Institute of Technology (IIT) Delhi |
2 years |
Master's |
| M.Tech in Data Science and Machine Learning |
Indian Institute of Technology (IIT) Madras |
2 years |
Master's |
| M.Tech in Artificial Intelligence |
Indian Institute of Technology (IIT) Hyderabad |
2 years |
Master's |
| M.Tech in Machine Learning and Data Analytics |
Indian Institute of Technology (IIT) Kharagpur |
2 years |
Master's |
| M.Tech in Data Science and Machine Learning |
Indian Institute of Science (IISc) Bangalore |
2 years |
Master's |
| M.Tech in Artificial Intelligence and Machine Learning |
National Institute of Technology (NIT) Trichy |
2 years |
Master's |
| M.Tech in Artificial Intelligence and Machine Learning |
International Institute of Information Technology (IIIT) Hyderabad |
2 years |
Master's |
| M.Tech in Data Science and Machine Learning |
Birla Institute of Technology and Science (BITS) Pilani |
2 years |
Master's |
| M.Tech in Machine Learning |
Amrita Vishwa Vidyapeetham, Coimbatore |
2 years |
Master's |
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Machine Learning Courses & Certifications Online
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