Feb 17, 2023

The article lists the best rated and free machine learning courses to help you learn the industry relevant skills and make you job ready.

Machine learning is one of the most exciting areas of computer science and statistics, helping many industries become more efficient and intelligent. The job market is in need of skilled and knowledgeable professionals, but still faces a significant talent shortage. To be apart of this trending workforce, encourage you to learn machine learning. We have selected some top rated free machine learning courses to help you improve your skills. Let's Explore.

*You May Like : **Machine Learning - A Complete Guide.*

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- Machine Learning Course by fast.ai
- Machine Learning Course by deeplearning.ai
- Machine Learning Course A-Z™️: Hands-On Python & R In Data Science by Udemy
- Data Science: Machine Learning by Harvard University
- Machine Learning Specialization by coursera
- Advanced PGP in Data Science and Machine Learning by NIIT
- Machine Learning - Artificial Intelligence Course by Columbia University
- Machine Learning with Python by freecodecamp.org
- Machine Learning Course with R by DataCamp
- Machine Learning Courses by edX

We have followed the below criteria to pick the best free machine learning courses for you. The course –

- Focuses on the concepts and mathematical foundations of machine learning.
- Uses popular open-source programming languages, tools, and libraries.
- Provides programming assignments for hands-on practice.
- Balances theory with real-world applications.
- Includes hands-on projects and case studies.
- Taught by skilled, experienced, engaging, and personable instructors.
- Has a rating of at least 4.5 out of 5.
- Self-paced and on-demand.

*Explore – **Machine Learning Courses *

The course contains **Introduction to Machine Learning for Coders **taught by** Jeremy Howard. **In this course you will learn how to create machine learning models from scratch, as well as key skills in data preparation, model validation, and building data products.

The course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program. Before starting this course , you must have at least one year of coding experience and either have basic math or are prepared to do some independent study to refresh your knowledge.

**Duration : **There are approximately 24 hours of study and you should plan to spend about 8 hours per week for 12 weeks completing the material.

**Skill Level : **Intermediate

- Introduction to Random Forest
- Random Forest Deep Dive
- Performance, Validation and Model Interpretation
- Feature Importance, Tree Interpreter
- Exploration and RF from Scratch
- Data Product and Live Coding
- RF from Scratch and Gradient Descent
- Gradient Descent and Logistic Regression
- Regularization, Learning Rates and NLP
- More NLP and Columnar Data
- Embeddings
- Complete Rossmann , Ethical Issues

Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly Specialization is where you’ll master the fundamental mathematics toolkit of machine learning.

Many machine learning engineers and data scientists struggle with mathematics. Challenging interview questions often hold people back from leveling up in their careers, and even experienced practitioners can feel held by a lack of math skills.

This Specialization uses an innovative math pedagogy to help you learn quickly and intuitively, with courses that use plug-ins and easy-to-follow visualizations to help you see the math behind machine learning how it actually works. Upon completion, you'll understand the math behind all of the most popular algorithms and data analysis techniques, and the secret to incorporating them into your machine learning career.

**Duration : **Approximately 3 months to complete

**Skill Level : **Beginner Level

There are 3 Courses in this Specialization

**Course 1 : Linear Algebra for Machine Learning and Data Science**

- System of linear equations
- Solving system of linear equations
- Vectors and Linear Transformations
- Determinants and Eigenvectors

**Duration : **Approx. 21 hours to complete

**Skill Level : **Beginner Level

**Course 2 : Calculus for Machine Learning and Data Science**

- Derivatives and Optimization
- Gradients and Gradient Descent
- Optimization in Neural Networks and Newton's Method

**Duration : **Approx. 25 hours to complete

**Skill Level : **Beginner Level

**Course 3 : Probability & Statistics for Machine Learning & Data Science**

- Statistical Analysis
- Probability And Statistics
- Probability
- Statistical Hypothesis Testing
- Machine Learning (ML) Algorithms

**Duration : **It's coming in March

**Skill Level : **Beginner Level

This course provides an in-depth, hands-on introduction to machine learning using both Python and R. It has received good ratings from users for its practical approach and the variety of algorithms covered. It will help you learn complex theory, algorithms, and coding libraries in a simple way.

You'll able to build an army of powerful Machine Learning models and know how to combine them to solve any problem and handle specific topics like Reinforcement Learning, NLP and Deep Learning and advanced techniques like Dimensionality Reduction and many more.

**Duration : **42 hours on-demand video

**Skill Level : **Beginner Level

- Data Preprocessing
- Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest 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

This course is part of Harvard's data science professional certificate program and covers machine learning concepts and techniques. It has received positive ratings from users for its well-structured content and the depth of material covered.

In this course, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will also learn about training data and how to use datasets to discover potentially predictive relationships. As you build a movie recommendation system, you will learn how to train algorithms using training data so that you can predict the outcome of future datasets. You'll also learn about overtraining and techniques to avoid it, such as cross-validation. All of these skills are fundamental to machine learning.

**Duration : **8 weeks

**Skill Level : **Beginner Level

- Introduction to Machine Learning
- Machine Learning Basics
- Smoothing and Linear Regression for Prediction
- Cross-validation and k-Nearest Neighbors
- The Caret Package
- Model Fitting and Recommendation Systems
- Final Assessment and Course Wrap-Up

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.

It provides an extensive introduction to modern machine learning, including supervised learning (multivariate linear regression, logistic regression, neural networks and decision trees), unsupervised learning (clustering, reduction dimensions, recommendations) and some of the best practices used in Silicon Valley for artificial intelligence intelligence and machine learning innovation (model evaluation and tuning, applying a data-centric approach).data to improve performance, etc.)

By the end of this specialization, you'll master key concepts and gain the practical know-how to quickly and powerfully apply machine learning to complex real-world problems. If you are looking to enter the field of artificial intelligence or a career in machine learning, then the new Machine Learning major is the best place to start.

**Duration : **Approximately 3 months to complete

**Skill Level : **Beginner Level

There are 3 Courses in this Specialization

**Course 1 : Supervised Machine Learning: Regression and Classification**

- Introduction to Machine Learning
- Regression with multiple input variables
- Classification

**Duration :** Approx. 33 hours to complete

**Skill Level : **Beginner Level

**Course 2 : Advanced Learning Algorithms**

- Neural Networks
- Neural network training
- Advice for applying machine learning
- Decision trees

**Duration : **Approx. 34 hours to complete

**Skill Level : **Beginner Level

**Course 3 : Unsupervised Learning, Recommenders, Reinforcement Learning**

- Unsupervised learning
- Recommender systems
- Reinforcement learning

**Duration : **Approx. 27 hours to complete

**Skill Level : **Beginner Level

This program, designed in partnership with the Fraunhofer Research Foundation, equips you with the skills you need to become a confident, work-ready professional who can contribute to a wide range of activities in data science practice.

This program is designed to provide advanced training in data science and machine learning, covering both theory and practical applications. It has received good ratings from users for its comprehensive coverage and the hands-on approach.

**Duration : **18 Weeks

**Skill Level : **Beginner Level

**Program 1 : Data Science Professional for Beginner**

- Data Analysis and Data Structure using Excel
- Statistical tools and techniques
- Data Visualization
- Data Analysis with Python using Python libraries such as Pandas, NumPy

**Duration : **6 Weeks

**Program 2 : Data Science Professional for Intermediate**

- Source, validate, clean, store and query data and perform data analysis
- Create data dashboards & visualizations using Tableau
- Slice and dice data to generate hypotheses
- Use statistical tools to validate a hypothesis
- Create data and ML models for business forecasting and predictive analytics
- Apply story-telling techniques using data to engage with stakeholders and help in data-based business decision-making

**Duration : **6 Weeks

**Program 3 : Data Science Professional for Advance**

- Modelling data using machine learning tools and techniques
- Construct, define and validate the ML models for supported / automated decision making
- Use NLP to do analysis on textual data
- Complete a project including ML modelling: Business understanding -> Data preparation -> Data Analysis -> Prepare ML model -> Deploy ML Model -> Demo & Present Insights

**Duration : **6 Weeks

Machine learning is a growing field used for web research, ad placement, credit scoring, stock trading, and many other applications. This data science course is an introduction to machine learning and algorithms. You will develop a fundamental understanding of machine learning principles and come up with practical solutions using predictive analytics. We will also look at why algorithms play an important role in big data analysis.

This course covers the basics of machine learning and artificial intelligence, including concepts such as decision trees, neural networks, and deep learning. It has received good ratings from users for its clear explanations and the hands-on approach.

**Duration : **5 weeks

**Skill Level : **Beginner Level

- Introduction to the course
- Week 1: Algorithms 1
- Week 2: Algorithms 2
- Week 3: Algorithms 3 and Application to Personal Genomics
- Week 4: Machine Learning
- Week 5: Machine Learning Applications

This is a free course in machine learning, taught using Python. The course covers topics such as linear regression, decision trees, and random forests. The course is designed for beginners, and is available on the freecodecamp.org website.

In the Machine Learning with Python certification, you'll use the TensorFlow framework to build a variety of neural networks and explore more advanced techniques like natural language processing and reinforcement learning. You will also dive into neural networks and learn about the operating principles of deep, recurrent, and convolutional neural networks.

**Duration : **Approximately 2.5 months to complete

**Skill Level : **Beginner Level

- Introduction: Machine Learning Fundamentals
- Introduction to TensorFlow
- Core Learning Algorithms
- Neural Networks with TensorFlow
- Convolutional Neural Networks
- Natural Language Processing With RNNs
- Reinforcement Learning With Q-Learning

This course is offered by DataCamp, and is a comprehensive guide to machine learning in R. The course covers a wide range of topics, including supervised and unsupervised learning, and deep learning. The course is designed for those with a background in programming and R, and has received positive reviews for its clear explanations and practical applications.

You'll learn how to process data for modeling, train your models, visualize your models and assess their performance, and tune their parameters for better performance. In the process, you'll get an introduction to Bayesian statistics, natural language processing, and Spark.

**Duration : **14 Courses** , **Approximately 57 hrs

**Skill Level : **Intermediate to Advance

- Supervised Learning in R: Classification
- Supervised Learning in R: Regression
- Unsupervised Learning in R
- Machine Learning in the Tidyverse
- Intermediate Regression in R
- Cluster Analysis in R
- Machine Learning with caret in R
- Modeling with tidymodels in R
- Machine Learning with Tree-Based Models in R
- Effective learning starts with assessment
- Support Vector Machines in R
- Fundamentals of Bayesian Data Analysis in R
- Hyperparameter Tuning in R
- Bayesian Regression Modeling with rstanarm
- Introduction to Spark with sparklyr in R

edX offers a wide range of courses in machine learning, taught by top universities and institutions. The courses cover topics such as supervised learning, unsupervised learning, and deep learning. The courses are designed for those with a background in programming and math, and are taught in Python and R.

In the first half of the course, we will cover supervised learning techniques for regression and classification. In this framework we have an output or response that we want to predict based on a set of inputs. We will discuss some basic methods to accomplish this task and algorithms to optimize them. Our approach will be more practical, which means we will develop a full mathematical understanding of the respective algorithms, but we will only cover abstract learning theory briefly.

In the second half of the course, we move on to unsupervised learning techniques. In these problems, the less obvious end goal is to predict output based on a suitable input. We will discuss three basic problems in unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.

**Duration : **12 weeks

**Skill Level : **Beginner Level

**Week 1 :**Maximum likelihood estimation, linear regression, least squares**Week 2:**Ridge regression, bias-variance, Bayes rule, maximum a posteriori inference**Week 3:**Bayesian linear regression, sparsity, subset selection for linear regression**Week 4:**Nearest neighbor classification, Bayes classifiers, linear classifiers, perceptron**Week 5:**Logistic regression, Laplace approximation, kernel methods, Gaussian processes**Week 6:**Maximum margin, support vector machines, trees, random forests, boosting**Week 7:**Clustering, k-means, EM algorithm, missing data**Week 8:**Mixtures of Gaussians, matrix factorization**Week 9:**Non-negative matrix factorization, latent factor models, PCA and variations**Week 10:**Markov models, hidden Markov models**Week 11:**Continuous state-space models, association analysis**Week 12:**Model selection, next steps

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