The Best Machine Learning online courses and tutorials for beginners to learn Machine Learning in 2020.

Machine learning is changing the way we design and use our technology. It has slowly spread its reach through our devices, from self-driving cars to even automated chatbots. To define machine learning in the simplest terms, it is basically the ability to equip computers to think for themselves based on the scenarios that occur. This is the basis of artificial intelligence.

With machine learning as the future of technology, getting your hands on this type of development is crucial. However, it isn’t easy. Machine learning is a complex concept that uses algorithms to design coding that will help computers decipher a lot of data.

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Top Machine Learning Tutorials, Courses Certifications List

  1. Machine Learning

  2. Machine Learning A-Z Python & R in Data Science Course

  3. Machine Learning with TensorFlow on Google Cloud Platform

  4. Learn Python for Data Science, Structures, Algorithms, Interviews

  5. Artificial Intelligence and Machine Learning For Beginners

  6. Advanced Machine Learning

  7. Applied Machine Learning: Foundations Online Class

  8. Machine Learning in Python Data Science and Deep Learning

  9. Essential Math for Machine Learning: Python Edition

  10. Data Science and Machine Learning Curriculum Bootcamp with R

  11. Mathematics for Machine Learning

  12. Understanding Machine Learning

  13. Building Recommender Systems with Machine Learning and AI

  14. Designing a Machine Learning Model

  15. Become a Machine Learning Engineer

1. Machine Learning

In this course, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval.

Course rating: 4.7 out of 5.0 ( 20,939 Ratings total)

In this course, you will learn how to

  • identify potential applications of machine learning in practice. -
  • describe the core differences in analyses enabled by regression, classification, and clustering.
  • select the appropriate machine learning task for a potential application.
  • apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
  • represent your data as features to serve as input to machine learning models.
  • assess the model quality in terms of relevant error metrics for each task.
  • utilize a dataset to fit a model to analyze new data.
  • build an end-to-end application that uses machine learning at its core.
  • implement the mentioned techniques in Python.
  • describe the input and output of a regression model.
  • compare and contrast bias and variance when modeling data.
  • estimate model parameters using optimization algorithms.
  • tune parameters with cross-validation.
  • analyze the performance of the model.
  • describe the notion of sparsity and how LASSO leads to sparse solutions.
  • deploy methods to select between models.
  • exploit the model to form predictions.
  • build a regression model to predict prices using a housing dataset.
  • implement these techniques in Python.
  • describe the input and output of a classification model.
  • tackle both binary and multiclass classification problems.
  • implement a logistic regression model for large-scale classification.
  • create a non-linear model using decision trees.
  • improve the performance of any model using boosting.
  • scale your methods with stochastic gradient ascent.
  • describe the underlying decision boundaries.
  • build a classification model to predict sentiment in a product review dataset.
  • analyze financial data to predict loan defaults.
  • use techniques for handling missing data.
  • evaluate your models using precision-recall metrics.
  • create a document retrieval system using k-nearest neighbors.
  • identify various similarity metrics for text data.
  • reduce computations in k-nearest neighbor search by using KD-trees.
  • produce approximate nearest neighbors using locality sensitive hashing.
  • compare and contrast supervised and unsupervised learning tasks.
  • cluster documents by topic using k-means.
  • parallelize k-means using MapReduce.
  • examine probabilistic clustering approaches using mixtures models.
  • fit a mixture of Gaussian model using expectation maximization (EM).
  • perform mixed membership modeling using latent Dirichlet allocation (LDA).
  • describe the steps of a Gibbs sampler and how to use its output to draw inferences.
  • compare and contrast initialization techniques for non-convex optimization objectives.

You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. You will get hands-on experience with machine learning from a series of practical case-studies.

You will learn how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.

You will create models that predict a continuous value (price) from input features. Here, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity.

In this course, you will create models that predict a class (positive/negative sentiment) from input features. You will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees, and boosting.

In the fourth stage, you will examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce.

You can take the Machine Learning Certificate Course on Coursera.

2. Machine Learning A-Z Python & R in Data Science Course

In this course, you will learn how to create Machine Learning Algorithms in Python and R. You will also learn to develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

Course rating: 4.5 out of 5.0 ( 127,478 Ratings total)

In this course, you will learn how to:

  • master Machine Learning on Python & R.
  • have a great intuition of many Machine Learning models.
  • make accurate predictions.
  • make a powerful analysis.
  • make robust Machine Learning models.
  • create strong added value to your business.
  • use Machine Learning for personal purposes.
  • handle specific topics like Reinforcement Learning, NLP, and Deep Learning.
  • handle advanced techniques like Dimensionality Reduction.
  • know which Machine Learning model to choose for each type of problem.
  • build an army of powerful Machine Learning models and know-how to combine them to solve any problem.

The course includes:

  • 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

You can take Machine Learning A-Z (Python & R in Data Science Course) Certificate Course on Udemy.

3. Machine Learning with TensorFlow on Google Cloud Platform

In this course, you will learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions.

Course rating: 4.5 out of 5.0 ( 14,603 Ratings total)

In this course, you will learn how to:

  • compare the key required aspects of a good feature.
  • preprocess and explore features with Cloud Dataflow and Cloud Dataprep.
  • combine and create new feature combinations through feature crosses.
  • understand and apply TensorFlow transforms features.
  • identify the true potential of deep learning.
  • optimize and evaluate models using loss functions and performance metrics.
  • mitigate common problems that arise in machine learning Create repeatable and scalable training, evaluation, and test datasets.
  • understand the key components of TensorFlow.
  • use the tf.data library to manipulate data and large datasets.
  • create machine learning models in TensorFlow.
  • use Keras sequential and functional APIs for model creation with Tensorflow 2.x.
  • train, deploy and productionalize ML models at scale with Cloud AI Platform.

You will convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. You will also learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems.

You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.

In the second stage, you will learn how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; You will learn about methods of doing so in a repeatable way that supports experimentation.

In the third stage, you will learn how to create machine learning models in TensorFlow which is the tool you will use to write machine learning programs. You will learn how to use the TensorFlow libraries to solve numerical problems. Then, you will look at the Estimator API, which provides the highest level abstraction within TensorFlow for training, evaluating, and serving machine learning models.

Finally, you will learn how to execute TensorFlow models on Cloud ML Engine, Google-managed infrastructure to run TensorFlow. You will learn how to train, deploy, and productionalize ML models at scale with the Cloud Machine Learning Engine.

In the final stage, you will learn the many knobs and levers involved in training a model. You will first manually adjust them to see their effects on model performance. and you will learn how to automate them using Cloud Machine Learning Engine on Google Cloud Platform.

You can take Machine Learning with TensorFlow on Google Cloud Platform Certificate Course on Coursera.

4. Learn Python for Data Science, Structures, Algorithms, Interviews

In this course, you will learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, and Tensorflow.

Course rating: 4.6 out of 5.0 ( 82,580 Ratings total)

In this course, you will learn about:

  • using Python for Data Science and Machine Learning.
  • using Spark for Big Data Analysis.
  • implementing Machine Learning Algorithms.
  • using NumPy for Numerical Data.
  • using Pandas for Data Analysis.
  • using Matplotlib for Python Plotting.
  • using Seaborn for statistical plots.
  • using Plotly for interactive dynamic visualizations.
  • using SciKit-Learn for Machine Learning Tasks.
  • K-Means Clustering.
  • Logistic Regression.
  • Linear Regression.
  • Random Forest and Decision Trees.
  • Natural Language Processing and Spam Filters.
  • Neural Networks.
  • Support Vector Machines.

You will learn to analyze data, create visualizations, and use powerful machine learning algorithms. Finally, you will learn how to program with Python, creating data visualizations, and using Machine Learning with Python.

The course includes:

  • Programming with Python
  • NumPy with Python
  • Using pandas Data Frames to solve complex tasks
  • Use pandas to handle Excel Files
  • Web scraping with python
  • Connect Python to SQL
  • Use matplotlib and seaborn for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with SciKit Learn, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing
  • Neural Nets and Deep Learning
  • Support Vector Machines

You can take Learn Python for Data Science, Structures, Algorithms, Interviews Certificate Course on Udemy.

5. Artificial Intelligence and Machine Learning For Beginners

This course will help you breakdown machine learning into simple and easy to understand concepts. The course covers a variety of different machine learning concepts such as supervised learning, unsupervised learning, reinforced learning, and even neural networks.

Course rating: 4.3 out of 5.0 ( 643 Ratings total)

In this course, you will learn about:

  • Machine learning.
  • breakdown of important concepts required in machine learning.
  • different types of machine learning.
  • detailed analysis of the different types of machine learning such as unsupervised learning, supervised learning, reinforcement learning, and neural networks.
  • how to integrate the algorithms in actual Python Projects.

In addition to understanding the theory behind machine learning, you will then actually use these concepts and implement them into actual projects to see how they work in action.

You will understand machine learning algorithms and even start writing your own algorithms that you can use for your own projects.

You can take Artificial Intelligence and Machine Learning For Beginners Certificate Course on Eduonix.

6. Advanced Machine Learning

The goal of this course is to give learners a basic understanding of modern neural networks and their applications in computer vision and natural language understanding.

Course rating: 4.6 out of 5.0 ( 1,526 Ratings total)

In this course, you will:

  • understand how to solve predictive modeling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks.
  • learn how to preprocess the data and generate new features from various sources such as text and images.
  • be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures, or finding nearest neighbors as a means to improve your predictions.
  • be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data.
  • gain experience of analyzing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors, and other data-related issues such as leakages and you will learn how to overcome them.
  • acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance.
  • master the art of combining different machine learning models and learn how to ensemble.
  • get exposed to past (winning) solutions and codes and learn how to read them.
  • learn about the foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc.

The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. You will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers and you will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks.

In the course, you will implement a deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. In the second stage, you will learn to analyze and solve competitively predictive modeling tasks.

In the third stage, you will learn about Bayesian methods of machine learning. When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money. Here, you will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it.

In the next stage, you will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing, and new image generation.

In the next stage, you will cover Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. You will learn to be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well.

In the final stage, you will be introduced to the main concepts of the Physics behind the Large Hadron Collider (LHC). Moreover, you will scrutinize the major stages of the data processing pipelines, and focus on the role of the Machine Learning techniques for such tasks as track pattern recognition, particle identification, online real-time processing (triggers) and search for very rare decays.

You can take the Advanced Machine Learning Certificate Course on Coursera.

7. Applied Machine Learning: Foundations Online Class

In this course, you will dig into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples.

Course rating: 32,684 total enrollments

In this course, y0u will learn:

  • about the foundations of Machine learning.
  • exploratory data analysis.
  • evaluating a model to ensure it generalizes to unseen examples.

The course includes:

  • Machine Learning Basics
  • Exploratory Data Analysis and Data Cleaning
  • Measuring Success
  • Optimizing a Model
  • End-to-end Pipeline

You can take Applied Machine Learning: Foundations Online Class Certificate Course on LinkedIn.

8. Machine Learning in Python Data Science and Deep Learning

This course is a hands-on Machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks.

Course rating: 4.5 out of 5.0 ( 21,748 Ratings total)

In this course, you will learn how to:

  • build artificial neural networks with Tensorflow and Keras.
  • classify images, data, and sentiments using deep learning.
  • make predictions using linear regression, polynomial regression, and multivariate regression.
  • perform Data Visualization with MatPlotLib and Seaborn.
  • implement machine learning at a massive scale with Apache Spark's MLLib.
  • understand reinforcement learning - and how to build a Pac-Man bot.
  • classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA.
  • use train/test and K-Fold cross-validation to choose and tune your models.
  • build a movie recommender system using item-based and user-based collaborative filtering.
  • clean your input data to remove outliers.
  • design and evaluate A/B tests using T-Tests and P-Values.

The course includes:

  • Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras
  • Data Visualization in Python with MatPlotLib and Seaborn
  • Transfer Learning
  • Sentiment analysis
  • Image recognition and classification
  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross-validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multiple Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests
  • Feature Engineering
  • Hyperparameter Tuning

You can take Machine Learning in Python (Data Science and Deep Learning) Certificate Course on Udemy.

9. Essential Math for Machine Learning: Python Edition

This course demystifies the essential math that you need to grasp—and implement—in order to write machine learning algorithms in Python.

Course rating: 27,728 total enrollments

In this course, you will:

  • review fundamental algebraic concepts.
  • understand derivatives and optimization.
  • learn about the basics of probability.

The course includes:

  • Equations, Graphs, and Functions
  • Derivatives and Optimization
  • Vectors and Matrices
  • Statistics and Probability

You can take Essential Math for Machine Learning: Python Edition Certificate Course on LinkedIn.

10. Data Science and Machine Learning Curriculum Bootcamp with R

In this course, you will learn how to use the R programming language for data science and machine learning and data visualization.

Course rating: 4.6 out of 5.0 ( 11,364 Ratings total)

In this course, you will learn how to:

  • program in R.
  • use R for Data Analysis.
  • create Data Visualizations.
  • use R to handle CSV, excel, SQL files, or web scraping.
  • use R to manipulate data easily.
  • use R for Machine Learning Algorithms.
  • use R for Data Science.

The course includes:

  • Programming with R
  • Advanced R Features
  • Using R Data Frames to solve complex tasks
  • Use R to handle Excel Files
  • Web scraping with R
  • Connect R to SQL
  • Use ggplot2 for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with R, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Data Mining Twitter
  • Neural Nets and Deep Learning
  • Support Vector Machines

You can take the Data Science and Machine Learning Curriculum Bootcamp with R Certificate Course on Udemy.

11. Mathematics for Machine Learning

In the first stage, you will learn about Linear Algebra and look at what is is and how it relates to vectors and matrices. Then you will look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems.

Finally, you will look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

Course rating: 4.4 out of 5.0 ( 12,671 Ratings total)

In this course, you will learn how to:

  • implement mathematical concepts using real-world data.
  • derive PCA from a projection perspective.
  • understand the working of orthogonal projections.
  • master PCA.
  • write code blocks and encounter Jupyter notebooks in Python.
  • have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems.
  • use calculus to build approximations to functions.
  • apply the mentioned concepts to machine learning.
  • be familiar with important mathematical concepts and implement PCA all by yourself.

In the second stage, you will have a brief introduction to the multivariate calculus required to build many common machine learning techniques. You will start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function.

You will then start to build up a set of tools for making calculus easier and faster. Next, you will learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game.

In the third stage, you will be acquainted with the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique.

You will cover some basic statistics of data sets, such as mean values and variances, you will also compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, you will then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.

You can take Mathematics for Machine Learning Certificate Course on Coursera.

12. Understanding Machine Learning

In this course, you will learn about Machine learning from basics and explore the open-source programming language R. You will also learn about training and testing a model as well as using a model.

Course rating: 4.5 out of 5.0 ( 1,879 Ratings total)

In this course, you will learn:

  • about the fundamentals of Machine learning.
  • explore the open-source programming language R.

You can take Understanding Machine Learning Certificate Course on Pluralsight.

13. Building Recommender Systems with Machine Learning and AI

In this course, you will discover how to build your own recommender systems from one of the pioneers in the field. It covers recommendation algorithms based on neighborhood-based collaborative filtering and more modern techniques, including matrix factorization and even deep learning with artificial neural networks.

Course rating: 21,467 total enrollments

In this course, you will learn how to:

  • build recommender systems.
  • help people discover new products.
  • content with deep learning, neural networks, and machine learning recommendations.

The course includes:

  • Introduction to Python
  • Evaluating Recommender Systems
  • A Recommender Engine Framework
  • Content-Based Filtering
  • Neighborhood-Based Collaborative Filtering
  • Matrix Factorization Methods
  • Introduction to Deep Learning
  • Deep Learning for Recommender Systems
  • Scaling It Up
  • Real-World Challenges of Recommender Systems
  • Hybrid Approaches

Along the way, you can learn from and understand the real-world challenges of applying the algorithms at a large scale with real-world data. You can also go hands-on, developing your own framework to test algorithms and building your own neural networks using technologies like Amazon DSSTNE, AWS SageMaker, and TensorFlow.

You can take Building Recommender Systems with Machine Learning and AI Certificate Course on LinkedIn.

14. Designing a Machine Learning Model

In this course, you will gain the ability to appropriately frame your use-case and then choose the right solution technique to model it.

You will learn how rule-based systems and ML systems differ and how traditional and deep learning models work. Next, you will discover how supervised, unsupervised, and reinforcement learning techniques differ from each other.

Course rating: 4.5 out of 5.0 ( 23 Ratings total)

In this course, you will learn how to:

  • understand the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques.
  • classic supervised learning techniques such as regression and classification complement classic unsupervised techniques such as clustering and dimensionality reduction.

This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.

You will learn how classic supervised learning techniques such as regression and classification complement classic unsupervised techniques such as clustering and dimensionality reduction.

You will then understand the assumptions and outcomes of these techniques and how solutions can be evaluated. Finally, you will round out your knowledge by designing end-to-end ML workflows for canonical ML problems, ensemble learning, and neural networks.

You can take Designing a Machine Learning Model Certificate Course on Pluralsight.

15. Become a Machine Learning Engineer

In this course, you will learn advanced machine learning techniques and algorithms on your way to becoming a machine learning engineer.

You will learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment.

In this course, you will learn how to:

  • write production-level code and practice object-oriented programming, which you can integrate into machine learning projects.
  • deploy machine learning models to a production environment using Amazon SageMaker.
  • apply machine learning techniques to solve real-world tasks
  • explore data and deploy both built-in and custom-made Amazon SageMaker models.
  • propose a possible solution for a machine learning challenge.

You will gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in the industry.

You can take Become a Machine Learning Engineer Certificate Course on Udacity.


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