Machine learning is, more or less, a way for computers to learn things without being specifically programmed. As humans, we learn through past experiences. We use our senses to obtain these "experiences" and use them later to survive. Machines learn through commands provided by humans. These sets of rules are known as algorithms.

Here's another mindmap which focuses only on Deep Learning

Everything you need to know about Reinforcement LearningApril 4, 2022 The phrase "Reinforcement Learning" could sound a little intimidating at first, but when we break it down, it's actually quite simple. Let's start with the phrase itself. It simply means to strengthen or support something. The phrase "Reinforcement Learning" could sound a little intimidating at first, but when we break it down, it's actually quite simple. Let's start with the phrase itself.

Data science interviews can be tough to navigate. The fact that it's such a multi-disciplinary field means that the sheer volume of material you need to cover to feel properly prepared can become overwhelming. Here I summarize most queries and put in "Cheatsheet" format, and hope this could help readers to cracking the data science interviews.

Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant Learn data cleaning, processing, wrangling and manipulation How to create resume and land your first job as a Data Scientist How to use Python for Data Science How to write complex Python programs for practical industry scenarios Learn Plotting in Python (graphs, charts, plots, histograms etc) Learn to use NumPy for Numerical Data Machine Learning and it's various practical applications Supervised vs Unsupervised Machine Learning Learn Regression, Classification, Clustering and Sci-kit learn Machine Learning Concepts and Algorithms Use Python to clean, analyze, and visualize data Building Custom Data Solutions Statistics for Data Science Probability and Hypothesis Testing In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib NumPy -- A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library. Pandas -- A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.

Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. You'll start by learning how to use Jupyter Notebooks to improve the way you program with Python. After getting a good grounding in working with Python in Jupyter Notebooks, you'll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Notebooks allow you to apply concepts from earlier in the book. Those who are new to machine learning can dive in with these easy programs and develop basic skills.

Machine Learning is a Very Broad Field. If Machine Learning is a dish, then linear algebra, programming, analytical skills, statistics, and Algorithms are the primary recipes of Machine Learning. If you will go more deep inside the Machine Learning concepts, you will get confused about what to learn first or what to not focus much. So here, In this article, I will take you through the most important Machine Learning Concepts, which you need to keep as must-know concepts in machine learning. All Machine Learning concepts, that I have shown below are not based on the order of their rank or weightage in Machine Learning.

I recently finished watching this Machine Learning playlist (StatQuest by Josh Starmer) on Youtube and thought of summarizing each concept into a Q/A. As I prepare for more data science interviews, I thought it would be a good exercise to make sure that I am communicating my thoughts clearly and concisely during an interview. Let me know in the comments, if I am not doing a good job in explaining any of the concepts. NOTE: This article is not aimed for teaching a concept to beginners. It assumes that the reader has sufficient background in data science concepts.

In machine learning, the inputs that we have talked about above are called features. Features are a set of attributes assigned to a data point. The following example data set is a famous data set commonly used for machine learning practice problems known as "Boston housing prices". It consists of a set of features (highlighted red in the image below) relating to a house such as the age, average number of rooms and property tax values and a corresponding house price. For a machine learning model to be successful in performing its task a statistical relationship needs to exist between at least some of these features and the price of the house.

Although the existing guide is very good, it lacks a good explanation of the Decoder side. It simply says that it is very similar to the Encoder without really provide much detail. In my eyes, the whole "magic" of the transformer is the ability to have different input and output lengths! That part is poorly explained, simply saying that the Decoder is using the Key and Value of the Encoder is not enough because it doesn't explain how the different dimensions are mitigated.