Top 7 Machine Learning Courses to learn in 2022

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Machine Learning Engineers and NLP Scientists have bright careers. Machine learning makes various applications and industries smarter and more efficient. This class teaches math behind machine learning algorithms and their use in programming languages. Check out the list if you’re interested in this niche.

Top 7 Machine Learning Courses List

1. Machine Learning Course by Stanford

Machine Learning Course by Stanford

This curriculum teaches machine learning, support vector machines, kernels, neural networks, and related concepts. This specialization teaches AI and ML innovation methodologies.

Major topics covered:

  • Logistic Regression
  • Artificial Neural Network
  • Machine Learning Algorithms
  • Medical Informatics
  • Database Mining
  • Statistical Pattern Recognition
  • Regularization
  • Linear Regression With Multiple/One Variables

Prerequisites: Background knowledge in Machine Learning or relevant subject isn’t mandatory.

Level: Beginner
Rating: 4.9
Duration: 54 Hours (approximately)

2. Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z Hands-On Python & R In Data Science

Expert Data Scientists created this course to help students learn difficult algorithms and theories. Well-structured learning software is divided into Data Processing and Regression. Each tutorial helps learners build Machine Learning skills.

This program is fun and exciting for learning with covered topics:

  • Data Preprocessing
  • Regression
  • Clustering
  • Association Rule Learning
  • Upper Confidence Bound and Thompson Sampling
  • Natural Language Processing
  • Artificial Neural Networks
  • Dimensionality Reduction
  • Convolutional Neural Networks
  • Parameter Tuning,
  • K-fold Cross-Validation
  • Grid Search, and more.

Prerequisites: No technical knowledge is needed, except high school-level math.
Level: Beginner
Rating: 4.5
Duration: 44 Hours (approximately)

3. Machine Learning, Data Science, and Deep Learning with Python

Machine Learning, Data Science, and Deep Learning with Python

This specialty covers artificial neural networks, K-Means Clustering, etc. You’ll also learn Data Visualization with Seaborn and MatPlotLib and large-scale MLLib Apache Spark implementation.

Course topics:

  • Neural Networks and Deep Learning with Keras and TensorFlow
  • Transfer Learning
  • Image classification and recognition
  • Sentiment analysis
  • Multi-Level Models
  • Regression analysis
  • Multiple Regression
  • Random Forests and Decision Trees
  • A/B Tests and Experimental Design
  • Collaborative Filtering
  • Reinforcement Learning
  • Support Vector Machines
  • Feature Engineering

Prerequisites:

  • Linux, Mac, or Windows computers that can run new versions like Anaconda 3.
  • Prior experience in scripting or programming is mandatory.
  • You should be skilled in high school-level mathematics.

Level: Intermediate
Rating:4.5
Duration: 14 hours (approximately)

4. Machine Learning with Javascript

Machine Learning with Javascript

This Machine Learning course for Javascript developers will cover advanced memory profiling, constructing Tensorflow JS library apps, writing ML code, and other significant subjects.

You’ll also construct Node JS and browser-compatible apps. The program teaches Linear Algebra basics to speed up matrix-based routines.

This course covers:

  • Identifying Relevant Data
  • Recording Observation Data
  • Algorithms Overview
  • Tensor Concatenation
  • Applications of Tensorflow
  • Linear Regression
  • Matrix Multiplication
  • Vectorized Solutions for increasing performance
  • Plotting MSE Values with Javascript
  • Logistic Regression
  • Stochastic and Batch Gradient Descent

Prerequisites:

  • Fundamental knowledge of command and terminal line usage.
  • The capability of handling basic equations of math.

Level: Intermediate
Rating: 4.7
Duration:17.5 hours (approximately)

5. The Complete Machine Learning Course with Python

The Complete Machine Learning Course with Python

If you’re looking for a Machine Learning course, this is it. You’ll distinguish between machine learning, classical programming, and deep learning. You’ll learn about neural networks, tensor operations, validation, dropout, testing, regularisation, and under and overfitting.

This program covers the following:

  • Linear Regression with Scikit-Learn
  • Robust Regression
  • Data Preprocessing
  • Cross-validation
  • Logistic Regression
  • Confusion Matrix
  • Concepts of Support Vector Machine
  • Radial Basis Function
  • Linear SVM Classification
  • Visualizing Boundary
  • Ensemble Machine Learning Methods
  • Gradient Boosting Machine
  • kNN introduction
  • Dimensionality Reduction Concept
  • Clustering

Prerequisites:

Level: Beginner-Intermediate
Rating: 4.3
Duration: 17.5 Hours (approximately)

6. Data Science: Machine Learning

Data Science Machine Learning

This Harvard concentration teaches machine learning and its technological problems. This course digs further into ML’s data science approaches than others.

This course’s themes are:

  • Machine Learning Basics
  • Principal Component Analysis
  • Machine Learning Algorithms
  • Building Recommendation System
  • Regularization and its uses
  • Cross-Validation

Prerequisites: None
Level: Beginner
Rating: 4.3
Duration: 
8 weeks – 2-4 hours per week (approximately)

7. Intro to Machine Learning with PyTorch

Intro to Machine Learning with PyTorch

This Udacity Nanodegree program teaches supervised models, data cleansing, and machine learning techniques. Candidates can investigate unsupervised and deep learning. Each step of the training offers real experience through code projects and exercises.

Course topics include:

  • Model Construction
  • Neural Network Design
  • Pytorch Training
  • Unsupervised Learning Method Implementation
  • Deep Learning

Prerequisites: Fundamental knowledge of Python programming is needed.
Level: Intermediate (3 months access)
Rating: 4.3
Duration
– 3 months / 10 hours per week (approximately)

Conclusion

Machine learning allows people to experiment with their abilities and knowledge. Start your career by learning ML and associated subjects. Start with one of the aforementioned specialties. These Machine Study courses are affordable and offer anytime, anywhere learning.

Share your experiences with us and suggest your favorite course and why. Do you propose another course? Discuss!

rahul kumar

Rahul Kumar has long worked as a freelance writer. He works as a senior SEO and content marketing analyst at a digital marketing agency that specializes in content and data-driven SEO. He has over 5 years of expertise in digital marketing as well as affiliate marketing. He enjoys sharing his expertise in a variety of fields, including ecommerce, startups, social media marketing, make money online, affiliate marketing, and human resource management, among others. He has contributed to a number of authoritative SEO, Make Money Online, and digital marketing blogs.

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