Data Science is the science of providing meaningful insights on large amounts of complex data (Big Data). It combines different fields of work in statistics and computation to interpret data for decision-making process. Now we are done with the boring definition, let's talk about the cool stuff. Data Science has a lot of applications that are game changers almost in every industry from medicine to sports. Data Science is an exciting career path and one of the highly paid careers and here is how to become a data scientist in 2022.Last week, I spoke about the two main programming languages used and statistics needed to build foundation for data science. This week will be machine learning oriented; however, I will add a bonus part where I will talk about SQL and linear algebra and they will say why will you need them in Data Science but first let's talk about machine learning.
What is Machine Learning?
Machine Learning is the practice of using an algorithm to extract data, learn from it, and then forecast future trends for that topic. Traditional machine learning is comprised of statistical analysis and predictive analysis that is used to spot patterns and catch hidden insights based on perceived data. Different types of machine learning is supervised, unsupervised, semi-supervised, and reinforcement learning which I will talk about all of them briefly. Supervised learning can apply what has been learned in the past to new data using labeled examples to predict future events.Example: Predicting housing price. Unsupervised learning are used when the information used to train is neither classified nor labeled. Example: dogs vs cats classification. Semi-supervised learning fall somewhere between both supervised and unsupervised learning as it uses a small amount of labeled data and large amount of unlabeled data. Example: Speech Analysis. Finally, Reinforcement learning is a learning method that interacts with its environment by producing actions or discovers errors or rewards. Example: Anomaly Detection.
Difference between Machine Learning and Data Science
Data science is a broad term for multiple disciplines, machine learning fits within data science. The main difference between is machine learning focuses on algorithms and statistics while data science focuses on the entire data processing methodology that includes data cleaning, data visualization, and data analysis.
Best Free Machine Learning courses
Machine learning has a lot of online courses on different MOOCs(like Coursera and edx);however, I want to use a different approach. I am going to best library of videos: YouTube, because this is mainly the way I learned Machine Learning and there's a lot of videos to help with things like Computer Vision, Raspberry Pi and much more. So, Let's get started.
Tech with Tim
Tech with Tim channel offers different tutorials in programming field from making games with pygame to Android app development. But we are here to speak about machine learning, here comes the Tech with Tim Machine Learning and AI Mega Course. This course is four part course and in each step you will learn something new. In step 1, you learn the fundamentals. Step 2 will learn about neural network and Step 3 and 4 will make a chat bot and create neural network to play Flappy Bird. It is awesome course for any beginner
Link to course: Mega AI,ML course
Sentdex
Sentdex channel is machine learning heavy. He has a playlist of 73 videos dedicated to get you from beginner to intermediate. If you are interested, He also has courses on facial recognition using computer vision and Python plays GTA V which is a good watch.
Link to course: Sentdex Course
Stanford Andrew Ng ML Course
This is a weird one. This course is offered mainly by Coursera with a 7 day trial but the entire course is available on YouTube. It includes one of the bonus points: Linear Algebra. Since most of work as Machine Learning with matrices, Linear Algebra is a must have. This course is the best machine learning course you can take and it is rated by a lot of machine learning enthusiasts.
Link to course:Andrew Ng's course
Final point(bonus): SQL
As I looked through Data Science job description, I saw that most of these job descriptions included "Intensive SQL knowledge" which will be needed if you are going to work as a data scientist so I will include this Khan Academy course about how to work with and join databases
Link to course: Khan Academy SQL
And this wraps up the second part of three part mini-series. Next week, I will speak about Kaggle, and the importance Data Science and on Wednesday I will post my first Data Science Football project. Stay tuned.
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