MachineLearning Week-1

Hi everyone, welcome to the first article about my experiences, lessons, projects, and challenges from this first week in Machine Learning. This blog is divided into six sections, so you can focus on the parts that interest you:

1. Things learnt this week.

2. Steps I followed.

3. Problems encountered.

4. How I solved those problems.

5. Projects made this week.

6. Resources used.

1. Things Learnt this Week

Machine learning , a buzzword that is gaining a lot of attention nowadays. This week I started learning about pre-processing ,rather than just reading about theory , I created a Google colab notebook of my learnings . I started with different tools for precprocessing such as :-
1. Importing the libraries
2.Importing the dataset
3.Taking care of missing data
4. Encoding Categorical data
5. Splitting the dataset into test set and training set
6. Feature Scaling

After the first preprocessing steps , I made my first Machine Learning model which was a Simple linear regression model. The dataset was based on salary vs years of experience, where years of experience was the feature and salary was dependent variable . Learnt about different methods by which we can visualize the data.

2. Steps I Followed

I first learnt some basic introduction and capabilities of ai from MS Learn pathway , people underestimate simple basic articles but a chill read with MS learn made the base for introduction and what to expect in the way ahead. Pro tip:- Don’t waste too much time on theory and not actually building them, as we say theory can take you only so far …After the theoretical part , By the help of instructors and superdatascience I started with learning preprocessing tools , learnt about how they work in depth from google and then finalised my knowledge by preprocessing a basic dataset .

After the preprocessing steps were learnt , I made my first ML model which was a linear regression model , during plotting the results it demanded focus on values and plots ,but it was a fun activity overall.

3. Problems Encountered

1. Learning about new methods:-

Since in ML we can’t really answer why the given syntax is like that, the crucial step is to understand where to implement that method and why is actually the following method being used ,what are its parameters and what does the parameter expects as input.

2. Confusion and Imposter Syndrome:-

Because I am learning building the model step by step, many a times it feels like , I am underequipped and require more theoretical knowledge.

4. How I solved the Problems?

1. Learning about new methods:-

Just google it and you will get enough hold on what to do and what to expect. I remained curious , if the answer is not on google ,then let’s try gemini ,that’s how I learnt about them

2. Confusion and Imposter Syndrome:-

I just believed in myself and kept on moving ,it was the first week ,felt new and overwhelmed ,so let’s see how will the coming weeks go.

5. Colab Notebooks Prepared:-

You can find all the source codes on my GitHub profile, "Aditya07-builds"

1. Preprocessing toolkit :- All the tools that I learnt during learning preprocessing till now was uploaded here.

2. SimpleLinearRegression Model :- My first ML model is also present on the github , it included visualizing the data , fit and other methods.

6. Resources Used:-

1. W3school

2. Superdatascience

3. Udemy

4. Youtube

5. Gemini