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
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.
All the Best!
Enjoy Learning!
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Though of the Day…
‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.
– Henry Ford
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.