## Backdrop:

**Afew** days back, a friend of mine came laughing at me, saying “**what is taking you so long to learn machine learning? It’s just a few models, I learned them in a week**”. Those were his exact words. I simply smiled at him and inquired what he had learned. He told the names of a few machine learning algorithms. I asked him what exactly he learned, then came the obvious reply of using Sklearn’s fit and predict methods and a brief overview of how that algorithm works.

With an evil smile on my face, I asked him what he would do to get the best parameters? How does the model learn the best weights? What do we do when we have a low latency requirement? Though these are not some complex questions, He was sitting there silently watching my face, I had the last laugh.

The takeaway from the story is that machine learning is way beyond a simple fit and predict methods.

Most of us just watch a few videos on youtube and claim we know machine learning, only to realize our mistake pretty quickly. To all the people who are self-learning, just **remember that there are hundreds of thousands of people like you and me learning Machine Learning/Data Science**. Remember that we will be competing with people who have a Masters/Ph.D. in fields related to Data Science. So to be competitive with them, we need to be **really strong** with our fundamentals.

With a lot of buzz around Machine learning, new courses are popping out every day, there are more courses out there than the actual jobs at the moment. With this humongous resources there comes the dilemma to pick the right course.

To be honest, most of these courses are Mediocre and don’t cover in-depth. well, there are few good courses out there but each of them is offering a different curriculum. Few covers the math part in-depth few are good at the coding part and so on. So today I won’t be mentioning any course in particular. I am going to share the approach which I followed and suggested to the same friend in the story. I believe it should help you with your data science journey too.

## Introduction:

Before we jump into the algorithms part, let me tell you where exactly we use them in a Machine learning project. There are many phases involved in completing a Machine learning project and each phase is equally important.

### 1. What is the intuition behind the algorithm

### 2. How does the algorithm work?

### 3. Where it can be used / Where it cannot be used

### 4. Why the interpretability of an Algorithm is important

### 5. Why learn the Time/Space Complexity of an Algorithm?

### 6. Why do we need to understand the math behind an algorithm?

### 7. Why Implement it from Scratch (Optional):

#data-science #algorithms #education #ai