Machine learning has become one of the most in-demand skills in the job market and for good reason. With the ever-increasing amount of data available, organizations are seeking professionals who can use this data to make informed decisions and predictions. If you're interested in learning machine learning, taking an online course can be a great way to get started. In this article, we'll be discussing some of the best online Machine Learning courses available.
Best Online Machine Learning Course
No matter what your level of experience is, there is an online Machine Learning course out there for you. By taking one of these courses, you can gain the skills and knowledge needed to succeed in this exciting field.
Course | Provider | You will learn | Cost | Link |
|---|---|---|---|---|
Unsupervised Learning, Recommenders, Reinforcement Learning | Coursera | - Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection -Build recommender systems with a collaborative filtering approach and a content-based deep learning method -Build a deep reinforcement learning model | Free | |
Machine Learning A-Z: AI, Python & R + ChatGPT Bonus | Udemy | - Master Machine Learning on Python and R. -Make predictions, robust machine learning models and use for personal purpose. | Rs 649 | |
Python for Data Science and Machine Learning | Udemy | - Learn how to use NumPy, Pandas, Seaborn, MatPlotLib, Plotly, Scikit-Learn, Machine Learning, TensorFlow and more. | Rs 649 | |
Practical Machine Learning | Coursera | - Understand training and test sets, overfitting and error rates concepts. -Explain the complete process of building prediction functions | Free | |
Data Science: Machine Learning | edx | - Machine Learning basics - Perform cross-validation to avoid overtraining - How to build a recommendation system - What is regularization and why it is useful? | Free (Limited access to the course material and No certificate); Rs 8122 (Unlimited access to the course material and with Certificate) | |
Machine Learning in Python (Data Science and Deep Learning) | Udemy | - Build artificial neural networks with Tensorflow and Keras - Classify images, data, and sentiments using deep learning -Understand reinforcement learning - and how to build a Pac-Man bot | Rs 649 | |
Deep Learning A-Zâ„¢ 2023: Neural Networks, AI & ChatGPT Bonus | Udemy | - ANN, CNN, RNN. - Apply AutoEncoders in practice - Apply Boltzmann Machines in practice | Rs 649 | |
Machine Learning on Google Cloud Specialization | Coursera | - Use Vertex AI AutoML and BigQuery ML to build, train, and deploy ML models - Implement machine learning in the enterprise best practices - Describe how to perform exploratory data analysis and improve data quality | Free | |
Machine Learning Engineering for Production (MLOps) Specialization | Coursera | - Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements. - Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system. | Free |
1. Unsupervised Learning, Recommenders, Reinforcement Learning (Coursera)
Price:-Free | Duration - 27 hours | Level - Beginner
Instructor: Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig
Language: English
The unsupervised Learning, Recommenders, Reinforcement Learning course provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
Topics Covered:
Unsupervised learning algorithms: Clustering and anomaly detection.
Recommender systems
Ethical use of recommender systems.
TensorFlow implementation of content-based filtering
PCA algorithm
Reinforcement Learning
Making decisions: Policies in reinforcement learning
Bellman Equation
Learning the state-value function
Algorithm refinement: Improved neural network architecture
Algorithm refinement: ϵ-greedy policy
Link - https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning#instructors
2. Machine Learning A-Z: AI, Python & R + ChatGPT Bonus (Udemy)
Price - Rs 649 (Price varies) | Duration - 43 hours | Level - Beginner
Instructor - Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team and SuperDataScience Support
Language - German, English, Spanish, French, Indonesian, Italian, Portuguese, Arabic, Japanese, Thai, Turkish, Chinese, Korean
Machine Learning A-Z: AI, Python & R + ChatGPT Bonus course is designed by a data Scientist and a Machine Learning expert to develop new skills and improve the understanding of the challenging field of Data Science. This course can be completed by either doing either the Python tutorials, R tutorials, or both - Python & R. Pick the programming language that you need for your career. Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.
Topics covered:
Data Preprocessing
Regression: Simple Linear Regression, Multiple Linear, Regression, PolynomialRegression, SVR, Decision Tree Regression, Random Forest Regression
Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, RandomForest Classification
Clustering: K-Means, Hierarchical Clustering
Association Rule Learning: Apriori, Eclat
Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
Natural Language Processing: Bag-of-words model and algorithms for NLP
Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
Dimensionality Reduction: PCA, LDA, Kernel PCA
Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
3. Python for Data Science and Machine Learning (Udemy)
Price - Rs 649 (Price varies) | Duration - 25 hours | Level - Beginner
Instructor - Jose Portilla
Language - German, English, Spanish, French, Indonesian, Italian, Polish, Portuguese, Dutch, Arabic, Japanese, Thai, Turkish, Chinese, Korean
Python for Data Science and Machine Learning is designed for both beginners with some programming experience and experienced developers looking to make the jump to Data Science. With over 100 HD video lectures and detailed code notebooks for every lecture, this is one of the most comprehensive courses for data science and machine learning on Udemy. This course will teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python.
Topics Covered:
Use Python for Data Science and Machine Learning
Use Spark for Big Data Analysis
Implement Machine Learning Algorithms
Learn to use NumPy for Numerical Data, Pandas for Data Analysis, Matplotlib for Python Plotting and Seaborn for statistical plots
Use Plotly for interactive dynamic visualizations
Use SciKit-Learn for Machine Learning Tasks
K-Means Clustering
Logistic Regression and Linear Regression
Random Forest and Decision Trees
Natural Language Processing, Spam Filters and Neural Networks
Support Vector Machines
4. Practical Machine Learning (Coursera)
Price - Free course with Certificate | Duration - 8 hours (approx) | Level - Beginners to Intermediate
Instructors - Jeff Leek, Roger D. Peng, Brian Caffo
Language - Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
The practical Machine Learning course is designed to teach you the fundamental principles of constructing and utilizing prediction functions, with a focus on real-world applications. The course will offer a foundational understanding of essential concepts such as training and test sets, overfitting, and error rates. It will also introduce a variety of Machine Learning techniques, including model-based and algorithmic approaches such as regression, classification trees, Naive Bayes, and random forests. Additionally, this program will provide comprehensive instruction on the entire prediction function construction process, including data collection, feature creation, algorithm selection, and performance evaluation.
Topics covered:
What is Prediction? Prediction Study Design
In and Out of sample error and different types of errors.
receiver Operating Characteristic and Cross-Validation.
Introduction to Caret package
Tools for creating features and pre-processing
Machine Learning Algorithms
Regularized Regression and combining predictors
Forecasting and un-supervised Prediction
5. Data Science: Machine Learning (edx)
Price - Free (Limited access to the course material and No certificate); Rs 8122 (Unlimited access to the course material and with Certificate) | Duration - 8 weeks | Level - Beginners
Instructors - Rafael Irizarry
Language - English
Data Science: Machine Learning course will give you a brief introduction to Machine Learning Algorithms, principal components analysis and regularization by building a movie recommendation system. You will also learn about training data and how to use a set of data to discover potentially predictive relationships.
Topics covered:
Basics of Machine Learning
How to perform cross-validation to avoid over-training
Several popular Machine Learning algorithms
how to build a recommendations system
What is Regularization and why it is useful?
6. Machine Learning in Python (Data Science and Deep Learning) (Udemy)
Price - Rs 649 (Price varies) | Duration - 15 hours | Level -Intermediate
Instructor - Frank Kane, Sundog Education Team
Language - English, Bulgarian, Dutch, French, German, Geek, Spanish, Portugues, Romanian, Swedish, Thai, Ukrainian, Vietnamese
Machine Learning in Python (Data Science and Deep Learning) course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This course is best for software developers or programmers who want to transition into the data science and machine learning career path. Data analysts in finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.
Topics covered:
Statistics and Probability Refresher and Python Practice
Predictive Models
Machine Learning with Python
Recommender Systems
More Data Mining and Machine Learning Techniques
Dealing with Real-World Data
Apache Spark: Machine Learning on Big Data
Experimental Design/ML in the Real World
Deep Learning and Neural Networks
Generative Models
7. Deep Learning A-Zâ„¢ 2023: Neural Networks, AI & ChatGPT Bonus
Price - Rs 649 (Price varies) | Duration -22 house 33 minutes | Level - Beginner
Instructor - Kirill Eremenko, Hadelin De Ponteves, Ligency Team
Language - English, German, Indonesian, Italian, Japanese, Portuguese, Spanish, Thai, Turkish, Vietnamese and simplifies Chinese.
Deep Learning A-Zâ„¢ 2023: Neural Networks, AI & ChatGPT Bonus course has the great opportunity to work with both TensorFlow and PyTorch and understand when TensorFlow is better and when PyTorch is the better way to go. Throughout the tutorials, we compare the two and give you tips and ideas on which could work best in certain circumstances. This course is the best for students who want to start their career in Data Science. Also for those Data Analysts who want to level up their skills in Deep Learning.
Topics covered:
What is Deep Learning?
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Self-organizing maps
Boltzmann Machines
AutoEncoders
Get the Machine Learning Basics
regression & Classification Intuition
Data Preprocessing Template
Logistic Regression Implementation
8. Machine Learning on Google Cloud Specialization (Coursera)
Price - Free course with Certificate | Duration - 4 Months | Level - Intermediate
Instructor - Google Cloud
Language - English
Machine Learning on Google Cloud Specialization course will teach you how to build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models knowing basic SQL; create Vertex AI custom training jobs you deploy using containers (with little knowledge of Docker); use Feature Store for data management and governance; use feature engineering for model improvement; determine the appropriate data preprocessing options for your use case; use Vertex Vizier hyperparameter tuning to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems, write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud.
Topics covered:
Best practices for implementing machine learning on Google Cloud
What is Vertex AI and how can you use the platform to quickly build, train, and deploy AutoML machine learning models without writing a single line of code?
What is machine learning, and what kinds of problems can it solve?
How to improve data quality and perform exploratory data analysis.
Designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.
Good features and bad features of Feature Engineering and how you can preprocess and transform them for optimal use in your models.
A real-world practical approach to the ML Workflow.
9. Machine Learning Engineering for Production (MLOps) Specialization
Price - Free course with Certificate | Duration - 4 Months | Level - Advanced
Instructor - Andrew Ng, Cristian Bartolome Aramburu, Robert Crowe, Laurence Moroney
Language - English and French
The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentlessly evolving data. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.
Topics covered:
Overview of ML Lifecycle and Deployment
Selecting and Training a Model
Data Definition and Baseline
Collecting, Labeling, and Validating data
Feature Engineering, Transformation, and Selection
Data Journey and Data Storage
Advanced Data Labeling Methods, Data Augmentation, and Preprocessing of Different Data Types
Neural Architecture Search and Model Resource Management Techniques
High-Performance Modeling, Model Analysis and Interpretability
Model Serving Introduction and Model Serving Patterns and Infrastructures
Model Management and Delivery and Model Monitoring and Logging
Conclusion
Taking an online machine learning course is an excellent way to learn this valuable skill and stay competitive in the job market. With so many great options available, it can be challenging to choose the best course for your needs. Each course offers something unique, from in-depth programming assignments to hands-on projects and personalized feedback.