Machine Learning Bootcamp Series
Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more for beginners in Data Science!
Before starting the series, I assume your favorite programming language is installed, if not I recommend to watch this video for the entire setup.
What we will focus in this series🧐,
- Part-1: Machine Learning Fundamentals
- Part-2: Statistics
- Part-3: Data Preprocessing — Numpy, Pandas
- Part-4: Regression
- Part-5: Classification
- Part-6: Clustering
- Part-7: Association Rule Mining
- Part-8: Reinforcement Learning
- Part-9: Natural Language Processing
- Part-10: Deep Learning
- Part-11: Dimensionality Reduction
- Part-12: Model Selection & Boosting
From Part-2, we will write custom code templates where you can use it anywhere in your project.
In Part-1, we introduce the basics of machine learning theory, it’s applications & why ML is the future?
What is Machine Learning?
In simple terms, Machine Learning is an application of artificial intelligence where a computer or machine learns from its prior experiences (E) with respect to some class of tasks(T) and makes future predictions with a better performance(P) improving with experience E.
For better understanding of above terminologies, we will explain using an application of;
I reckon, everyone know about “Facebook Facial Recognition”, right? If not let me brief you, When you click a photo, Facebook(I mean Meta😅) somehow knows already who your friends are and tags them automatically, well that’s a ML algorithm running behind!!!
Task T: recognizing and classifying faces within images.
Performance measure P: percent of images correctly classified, i.e., accuracy
Experience E: a dataset of facial images with given labels (men, women)
In order to perform the task T, the system learns from the dataset provided.
A dataset is a collection of many samples, in our case, it’s images. A sample is a collection of features, i.e., eyes, nose, ear, etc. are our features.
Why ML is the future?
Here, we got a cloud, it simply illustrates that “data is everywhere” .
Few sources say, we are generating about 2.5 quintillion bytes of data every day😮 and as a data enthusiast what magic could be done with this amounts of data?
So, Machine Learning has the potential to use this data equipped with ML algorithms can step up to the challenge.
Note: Let us know about few types of variables in ML,
- Dependent(target/response): Output variable, i.e., the variable you want to predict
- Independent/Features: Input variables.
Machine Learning Categories
Machine Learning is generally categorized into three types:
Supervised Learning
In supervised learning the system/machine takes the examples along with the labels or targets for each example.
Classification and Regression are two of the most common supervised machine learning activities.
In regression problems, the target variable is continuous. Few examples are, sales prediction for a new product, Stock Predictions, predicting consumer behavior.
In classification problems, the target variable is discrete i.e., the machine must predict the most probable category, class, or label for new examples. Applications of classification include predicting whether a stock’s price will rise or fall, email is spam or not, deciding if a news article belongs to the politics or leisure section.
Unsupervised Learning
When we have unlabeled data i.e., no target variable, the machine attempts to uncover patterns from the data.
Reinforcement learning
Reinforcement learning is a type of ML method based on the rewarding system in which the machine receives a reward in the next time step to learn which action is best; this is known as the reinforcement signal. For example, maximize the points won in a game over many moves. Therefore, our goal here is to to maximize reward.
Conclusion
This series has been a tutorial to demonstrate Machine Learning for beginners and I’ll try to upload the articles as frequently as possible.
I hope you enjoyed it! Feel free to contact me for questions and feedback or just to share your interesting projects.
Contact me
Need help with Data Science? Contact me at prudhvithtavva@gmail.com
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