- Link: MLTut
- Publication date: March 10, 2024
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-
Table Of Contents
- Skills Required for Machine Learning-
- Resources to Learn ML-
- What does Machine Learning Engineer do?
- Roles and Responsibilities of Machine Learning Engineer
- Conclusion
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 Matthes Buy on Amazon or download PDF from here. 2. Head First Python: A Brain-Friendly Guide by Paul Barry Buy 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 Wickham Buy 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. Davies Buy on Amazon or download the PDF from here. |
| Mathematics | 1. An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani Buy this book on Amazon-An Introduction to Statistical Learning You can download the pdf version of this book from here. 2. Practical Statistics for Data Scientists by Peter Bruce Buy this book on Amazon-Practical Statistics for Data Scientists You can download the pdf version of this book from here. 3. Probability and Statistics for Data Science by Norman Matloff Buy this book on Amazon-Probability and Statistics for Data Science. 4. Introduction to Probability by Joseph K. Blitzstein, Jessica Hwang Buy 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 Deisenroth Buy 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 Géron Buy 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 Theobald Buy on Amazon or download from here. 4. Machine Learning: An Applied Mathematics Introduction by Paul Wilmott Buy on Amazon |
| 4. TensorFlow | 1. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers by Pete Warden Buy on Amazon 2. Adopting TensorFlow for Real-World AI by Mr. Naresh R. Jasotani Buy on Amazon 3. Advanced Deep Learning with TensorFlow 2 and Keras by Rowel Atienza Buy on Amazon |
| 5. Deep Learning | 1. Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow Buy on Amazon or download from here. 2. Deep Learning with Python by Francois Chollet Buy 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’s Buy on Amazon or download from here. |
Tutorials
YouTube Videos
| Topics | YouTube Videos |
|---|---|
| 1. Programming Languages (Python & R) | 1. CS DOJO 2. Programming with Mosh 3. Telusko 4. Clever Programmer 5. Corey Schafer 6. R Programming Tutorial– freeCodeCamp.org 7. R Programming Full Course– Simplilearn |
| 2. Mathematics | 1. Statistics for Data Science– Great Learning 2. Mathematics for Machine Learning [Full Course]– Edureka 3. Mathematics For Machine Learning- Simplilearn 4. Mathematics for Machine Learning– My CS |
| 3. Machine Learning Algorithms | 1. Machine Learning with Python– Great Learning 2. Machine Learning Tutorial Python– codebasics 3. Python Machine Learning Tutorial- Programming with Mosh 4. Machine Learning by Krish Naik |
| 4. TensorFlow | 1. TensorFlow 2.0 Complete Course– freeCodeCamp.org 2. TensorFlow Tutorial- Aladdin Persson 3. Coding TensorFlow– TensorFlow |
| 5. Deep Learning | 1. Complete Deep Learning–Krish Naik 2. Deep Learning With Tensorflow 2.0, Keras and Python– codebasics 3. 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.