Machine Learning is very powerful and popular. Many people are shifting their careers into the Machine learning field. But when it comes to learning machine learning, most of us are stuck and don’t know where to learn. That’s why I thought to collect and combine all the **best resources to learn machine learning online.**

So give your few minutes and find out the best resources to learn machine learning. You can bookmark this article so that you can refer to this article later.

Now without further ado, let’s get started-

**Best Resources to Learn Machine Learning Online**

Before discussing the resources, I would like to tell you what topics or skills you need to learn for Machine Learning-

**Skills Required for Machine Learning**–

**1. Programming Language**

Knowledge of Programming language is compulsory for machine learning. And the most popular programming languages are **Python, R, Java, and C++.** But as a beginner, you can start with Python.

**2. Mathematics Skill**

Knowledge of** Mathematics** is very important to understand how machine learning and its algorithms work. In math, the most important topics are-

- Probability and Statistics
- Linear Algebra
- Calculus

Now, let’s have a detailed look at all of them-

**a). Probability and Statistics**

Probability and statistics are used in Bayes’ Theorem, Probability Distribution, Sampling, and Hypothesis Testing.

**b). Linear Algebra**

Linear Algebra has two important terms- Matrices and Vectors. They are both used widely in Machine Learning. Matrices are used in **Image Recognition**.

**c). Calculus**

In Calculus, you have **Differential Calculus** and **Integral Calculus. **These terms help you to determine the **probability of events**. For example, finding the posterior probability in the Naive Bayes model.

**3. Machine Learning Algorithms**

You should know Machine Learning Algorithms like-

**Supervised Learning Algorithms**- Logistic Regression.
- K-Nearest Neighbors(K-NN)
- Support Vector Machine(SVM)
- Kernel SVM.
- Naive Bayes
- Decision Tree Classification.
- Random Forest Classification

**Unsupervised Learning Algorithms**- K-Means Clustering
- Hierarchical Clustering.
- Probabilistic Clustering

**Reinforcement Learning Algorithms**- Policy Optimization.
- Q-Learning
- Learn the Model
- Given the Model.

**4. Machine Learning Frameworks**

**Machine Learning Frameworks **make the life of developers and machine learning engineers a whole lot easier. ML Frameworks remove the complex part of machine learning and make it available for everyone who wants to use it.

These are some widely used Machine Learning Frameworks-

- TensorFlow.
- Theano.
- scikit learn.
- PyTorch.
- Keras.
- DL4J.
- Caffe.
- Microsoft Cognitive Toolkit.

**5. Data Engineering Skills**

For building a machine learning model, you need data for training and testing. That’s why knowledge of data engineering is important. Data Engineering contains 3 basic steps-

**Data pre-processing-**Data pre-processing step is performed before you process the data. Data pre-processing steps are**cleaning, parsing, correcting, and consolidating**the data.**ETL (Extract, Transform, and Load)-**In this step, you need to perform extraction of data from the internet or local server, then transform the data into a suitable format, and after that load the data into your program. That’s why you should have knowledge of ETL so that you can perform these steps easily.**Knowledge of Database-**You should be familiar with DBMS like SQL, Oracle Database, and No SQL.

**6. Deep Learning Algorithms**

**Deep learning** is the subpart of machine learning. And it is much more powerful than machine learning. Deep learning is getting more interest nowadays. That’s why you should be familiar with Deep Learning Algorithms.

The most used **Deep Learning Algorithms** are-

- Feedforward Neural Network.
- Backpropagation.
- Convolutional Neural Network.
- Recurrent Neural Network.
- Generative Adversarial Networks (GAN).

So, these are some must-have skills for Machine Learning, now let’s move to the best resources to learn machine learning online.

**Resources to Learn ML-**

For your convenience, I have created separate tables for each resource. So let’s start with online courses-

**Online Courses**

**Text Books**

Topics | Text Books |
---|---|

Programming Language (Python & R) | 1. Python Crash Course by Eric MatthesBuy on Amazon or download PDF from here.2. Head First Python: A Brain-Friendly Guide by Paul BarryBuy on Amazon or download PDF from here.3. Learn Python the Hard Way by Zed A. Shaw Buy on Amazon or download PDF from here.4. Automate the Boring Stuff with Python by Al Sweigart Buy on Amazon or download PDF from here.5. R for Data Science by Hadley WickhamBuy on Amazon or download PDF from here.6. Machine Learning with R by Brett Lantz Buy on Amazon 7. The Book of R: A First Course in Programming and Statistics by Tilman M. DaviesBuy on Amazon or download the PDF from here. |

Mathematics | 1. An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert TibshiraniBuy this book on Amazon- An Introduction to Statistical LearningYou can download the pdf version of this book from here.2. Practical Statistics for Data Scientists by Peter BruceBuy this book on Amazon- Practical Statistics for Data ScientistsYou can download the pdf version of this book from here.3. Probability and Statistics for Data Science by Norman MatloffBuy this book on Amazon- Probability and Statistics for Data Science.4. Introduction to Probability by Joseph K. Blitzstein, Jessica HwangBuy this book on Amazon- Introduction to Probability.You can download the pdf version of this book from here.5. Mathematics for Machine Learning by Marc Peter DeisenrothBuy on Amazon or download PDF from here.6. Linear Algebra and Optimization for Machine Learning by Charu C. Aggarwal Buy on Amazon or check the table of content from here. |

3. Machine Learning Algorithms | 1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien GéronBuy on Amazon or download from here.2. The Hundred-Page Machine Learning Book by Andriy Burkov Buy on Amazon or download from here.3. Machine Learning For Absolute Beginners by Oliver TheobaldBuy on Amazon or download from here.4. Machine Learning: An Applied Mathematics Introduction by Paul WilmottBuy on Amazon |

4. TensorFlow | 1. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers by Pete WardenBuy on Amazon2. Adopting TensorFlow for Real-World AI by Mr. Naresh R. JasotaniBuy on Amazon3. Advanced Deep Learning with TensorFlow 2 and Keras by Rowel AtienzaBuy on Amazon |

5. Deep Learning | 1. Deep Learning (Adaptive Computation and Machine Learning series) by Ian GoodfellowBuy on Amazon or download from here.2. Deep Learning with Python by Francois CholletBuy on Amazon or download from here.3. Neural Networks and Deep Learning by Charu C. Aggarwal Buy on Amazon or download from here. 4. Deep Learning: A Practitioner’s Approach by Adam Gibson and Josh Patterson’sBuy on Amazon or download from here. |

**Tutorials**

**YouTube Videos**

Topics | YouTube Videos |
---|---|

1. Programming Languages (Python & R) | 1. CS DOJO2. Programming with Mosh3. Telusko4. Clever Programmer5. Corey Schafer6. R Programming Tutorial– freeCodeCamp.org7. R Programming Full Course– Simplilearn |

2. Mathematics | 1. Statistics for Data Science– Great Learning2. Mathematics for Machine Learning [Full Course]– Edureka3. Mathematics For Machine Learning- Simplilearn4. Mathematics for Machine Learning– My CS |

3. Machine Learning Algorithms | 1. Machine Learning with Python– Great Learning2. Machine Learning Tutorial Python– codebasics3. Python Machine Learning Tutorial- Programming with Mosh4. Machine Learning by Krish Naik |

4. TensorFlow | 1. TensorFlow 2.0 Complete Course– freeCodeCamp.org2. TensorFlow Tutorial- Aladdin Persson3. Coding TensorFlow– TensorFlow |

5. Deep Learning | 1. Complete Deep Learning–Krish Naik2. Deep Learning With Tensorflow 2.0, Keras and Python– codebasics3. Deep learning Tutorial– Great Learning |

And here the list ends. I hope these resources will help you to learn and master machine learning. I would suggest you bookmark this article for future referrals.

**What does Machine Learning Engineer do?**

Machine Learning work with the following steps-

- Data Collection.
- Data Preprocessing.
- Choose a
**Machine Learning Algorithm**. - Training the Model.
- Testing the Model.
- Tuning the Model.

So, as a machine learning engineer, you have to perform all these steps.

Machine Learning Engineers create a **Machine Learning** model that can work properly with the best performance. Machine Learning Engineers have to choose the right algorithms as per model compatibility and requirement.

They have to extract ideas from the data science team, choose appropriate tools and ecosystems, Use machine Learning frameworks, and stay up to date with the latest development.

Now, let’s see the Roles and Responsibilities of Machine Learning Engineers-

**Roles and Responsibilities of Machine Learning Engineer**

- Study and convert Data Science Prototypes.
- Build
**Machine Learning**models. - Research and apply appropriate Machine Learning tools and algorithms.
- Build a Machine Learning application based on industry requirements.
- Choose correct datasets and data visualization methods.
- Conduct Machine Learning tests and experiments.
- Execute Statistical Analysis and fine-tuning with the help of test results. (Statistical Analysis is a small part of ML Engineers whereas it’s a major job part of Data Analyst).
- Train and Retrain the model based on model accuracy.
- Stay updated with the latest development in the field.

So, these are the Roles and responsibilities of the **Machine Learning Engineer**.

Now it’s time to wrap up this article “**Best Resources to Learn Machine Learning Online**“.

**Conclusion**

In this article, I tried to cover all the **Best Resources to Learn Machine Learning Online** from** online courses to YouTube videos**. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

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Though of the Day…

– Henry Ford

‘Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

### Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.