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Best Online Machine Learning Course

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-Z™ 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:

  1. Unsupervised learning algorithms: Clustering and anomaly detection.

  2. Recommender systems

  3. Ethical use of recommender systems.

  4. TensorFlow implementation of content-based filtering

  5. PCA algorithm

  6. Reinforcement Learning

  7. Making decisions: Policies in reinforcement learning

  8. Bellman Equation

  9. Learning the state-value function

  10. Algorithm refinement: Improved neural network architecture

  11. Algorithm refinement: ϵ-greedy policy


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:

  1. Data Preprocessing

  2. Regression: Simple Linear Regression, Multiple Linear, Regression, PolynomialRegression, SVR, Decision Tree Regression, Random Forest Regression

  3. Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, RandomForest Classification

  4. Clustering: K-Means, Hierarchical Clustering

  5. Association Rule Learning: Apriori, Eclat

  6. Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

  7. Natural Language Processing: Bag-of-words model and algorithms for NLP

  8. Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

  9. Dimensionality Reduction: PCA, LDA, Kernel PCA

  10. 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:

  1. Use Python for Data Science and Machine Learning

  2. Use Spark for Big Data Analysis

  3. Implement Machine Learning Algorithms

  4. Learn to use NumPy for Numerical Data, Pandas for Data Analysis, Matplotlib for Python Plotting and Seaborn for statistical plots

  5. Use Plotly for interactive dynamic visualizations

  6. Use SciKit-Learn for Machine Learning Tasks

  7. K-Means Clustering

  8. Logistic Regression and Linear Regression

  9. Random Forest and Decision Trees

  10. Natural Language Processing, Spam Filters and Neural Networks

  11. 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:

  1. What is Prediction? Prediction Study Design

  2. In and Out of sample error and different types of errors.

  3. receiver Operating Characteristic and Cross-Validation.

  4. Introduction to Caret package

  5. Tools for creating features and pre-processing

  6. Machine Learning Algorithms

  7. Regularized Regression and combining predictors

  8. 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:

  1. Basics of Machine Learning

  2. How to perform cross-validation to avoid over-training

  3. Several popular Machine Learning algorithms

  4. how to build a recommendations system

  5. 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:

  1. Statistics and Probability Refresher and Python Practice

  2. Predictive Models

  3. Machine Learning with Python

  4. Recommender Systems

  5. More Data Mining and Machine Learning Techniques

  6. Dealing with Real-World Data

  7. Apache Spark: Machine Learning on Big Data

  8. Experimental Design/ML in the Real World

  9. Deep Learning and Neural Networks

  10. Generative Models


7. Deep Learning A-Z™ 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-Z™ 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:

  1. What is Deep Learning?

  2. Artificial Neural Networks

  3. Convolutional Neural Networks

  4. Recurrent Neural Networks

  5. Self-organizing maps

  6. Boltzmann Machines

  7. AutoEncoders

  8. Get the Machine Learning Basics

  9. regression & Classification Intuition

  10. Data Preprocessing Template

  11. 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:

  1. Best practices for implementing machine learning on Google Cloud

  2. 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?

  3. What is machine learning, and what kinds of problems can it solve?

  4. How to improve data quality and perform exploratory data analysis.

  5. 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.

  6. Good features and bad features of Feature Engineering and how you can preprocess and transform them for optimal use in your models.

  7. 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:

  1. Overview of ML Lifecycle and Deployment

  2. Selecting and Training a Model

  3. Data Definition and Baseline

  4. Collecting, Labeling, and Validating data

  5. Feature Engineering, Transformation, and Selection

  6. Data Journey and Data Storage

  7. Advanced Data Labeling Methods, Data Augmentation, and Preprocessing of Different Data Types

  8. Neural Architecture Search and Model Resource Management Techniques

  9. High-Performance Modeling, Model Analysis and Interpretability

  10. Model Serving Introduction and Model Serving Patterns and Infrastructures

  11. 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.


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