neerajprasad209/100_days_mldl

Hello Data Enthusiast! I will be updating my 100-day Journey here along with detailed Code Files Starting from Essential Libraries to Advanced Machine Learning and Deep Learning Algorithm Theory with Implementation. Save for Later ⭐ Happy Learning :)

100 Days Machine Learning and Deep Learning

How it Started? Day 0 - 18 Sept 2023

Over the past months, I've dived into the world of data science, mastering tools like Pandas, NumPy, Matplotlib, Seaborn. Now, I'm ready to take my skills to the next level!

This 100-day journey will be all about understanding statistics, machine learning, and deep learning algorithms at their core, along with a lot of hands-on projects. I'm eager to delve deep into the theory behind these powerful algorithms, ensuring I grasp every concept intricately. But there's a twist!

Throughout this challenge, I'll be sharing my newfound insights with our amazing community. Each day, I'll revisit these topics and create articles to teach what I've learned. You can Follow me on Medium for Detailed Articles. My goal is simple: to enhance my own understanding while helping others on their data science journeys.

What Inspired Me?

One of the things is definitely the “Show Your Work” book by Austin Kleon, and I believe it can motivate you as well. Read more about it here.

Click Here to Find Detailed Articles.

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Daily Progress of 100 Days MLDL

DAY 1 (19 Sept 2023):

Topic: Pandas Revision through Handwritten Notes

1. Data Structures
2. Data Loading and Data Inspection
3. Data Selection and Indexing
4. Data Cleaning
5. Data Manipulation

Detailed Medium Article: Pandas Demystified: A Comprehensive Handbook for Data Enthusiasts

Detailed Source Code: Day 1 Commit

LinkedIn post: Day 1 Update

LeetCode Problems Solved:

1. Combine Two Tables
2. Second Highest Salary

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DAY 2 (20 Sept 2023):

Topic: Advanced Pandas Topics Revision

1. Data Aggregations
2. Data Visualizations
3. Time Series Data Handling
4. Handling Categorical Data
5. Advanced Topics

Detailed Medium Article: Advanced Pandas: A Comprehensive Handbook for Data Enthusiasts

Detailed Source Code: Day 2 Commit

LinkedIn post: Day 2 Update , Pandas Complete Guide Post

LeetCode Problems Solved:

1. Rank Scores
2. Nth Highest Salary
3. Duplicate Emails

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DAY 3 (21 Sept 2023):

Topic: Numpy Revision

1. Numpy Array Basics
2. Array Inspection
3. Array Operations
4. Working with Numpy Arrays
5. NumPy for Data Cleaning
6. NumPy for Statistical Analysis
7. NumPy for Linear Algebra
8. Advanced NumPy Techniques
9. Performance Optimization with NumPy

Detailed Medium Article: Mastering NumPy: A Data Enthusiast’s Essential Companion

Detailed Source Code: Day 3 Commit

LinkedIn post: Day 3 Update

LeetCode Problems Solved:

1. Median of Two Sorted Arrays
2. Consecutive Numbers

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DAY 4 (22 Sept 2023):

Topic: Matplotlib Fundamentals Revision

1. Basic Plotting
2. Plot Types
   • 2.1 Bar Chart
   • 2.2 Histograms
   • 2.3 Scatter plots
   • 2.4 Pie Charts
   • 2.5 Box Plot (Box and Whisker Plot)
   • 2.6 Heatmap, and Displaying Images
   • 2.7 Stack Plot

Detailed Medium Article: Mastering Maplotlib: A Comprehensive Guide to Data Visualization

Detailed Source Code: Day 4 Commit

LinkedIn post: Day 4 Update

LeetCode Problems Solved:

1. Employees Earning More Than Their Managers

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DAY 5 (23 Sept 2023):

Topic: Advanced Matplotlib Topics Revision

1. Multiple Subplots
   • 1.1 Creating Multiple Plots in a Single Figure
   • 1.2 Combining Different Types of Plots
2. Advanced Features
   • 2.1 Adding annotations and text
   • 2.2 Fill the Area Between Plots
   • 2.3 Plotting Time Series Data
   • 2.4 Creating 3D Plots
   • 2.5 Live Plot - Incorporating Animations and Interactivity.

Detailed Medium Article: Advanced Maplotlib: A Comprehensive Guide to Data Visualization

Detailed Source Code: Day 5 Commit

LinkedIn post: Day 5 Update

LeetCode Problem Solved:

1. Customers Who Never Order

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DAY 6 (24 Sept 2023):

Topic: Seaborn Fundamentals Revision

1. Categorical Plots
   • 1.1 Count Plot
   • 1.2 Swarm Plot
   • 1.3 Point Plot
   • 1.4 Cat Plot
   • 1.5 Categorical Box Plot
   • 1.6 Categorical Violin Plot

Detailed Source Code: Day 6 Commit

LinkedIn post: Day 6 Update

LeetCode Problem Solved:

1. Delete Duplicate Emails

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DAY 7 (25 Sept 2023):

Topic: Seaborn Univariate and Bivariate Plots

1. Univarite Plots
   • 1.1 KDE Plot
   • 1.2 Rug Plot
   • 1.3 Box Plot
   • 1.4 Violin Plot
   • 1.5 Strip Plot
2. Bivariate PLots
   • 2.1 Regression Plot
   • 2.2 Joint Plot
   • 2.3 Hexbin Plot

Detailed Medium Article: Mastering Seaborn: Demystifying the Complex Plots!

Detailed Source Code: Day 7 Commit

LinkedIn post: Day 7 Update

LeetCode Problem Solved:

1. Department Highest Salary

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DAY 8 (26 Sept 2023):

Topic: Seaborn Multivariate and Matrix Plots

1. Multivariate Plots
   • 1.1 Using Parameters
   • 1.2 Relational Plot
   • 1.3 Facet Grid
   • 1.4 Pair Plot
   • 1.5 Pair Grid
2. Matrix PLots
   • 2.1 Heat Map
   • 2.2 Cluster Map

Detailed Medium Article: Advanced Seaborn: Demystifying the Complex Plots!

Detailed Source Code: Day 8 Commit

LinkedIn post: Day 8 Update

LeetCode Problem Solved:

1. Rising Temparature

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DAY 9 (27 Sept 2023):

Topic: Plotly Fundamentals

1. Using plotly express to create basic plots
2. Using graph objects module to customize plots

Detailed Source Code: Day 9 Commit

LinkedIn post: Day 9 Update

LeetCode Problem Solved:

1. Game Play Analysis I

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DAY 10 (28 Sept 2023):

Topic: Plotly Advanced plots

1. Advanced Plots
   • Box plots
   • Violin Plots
   • Density Heatmaps
   • Scatter Matrix
   • 3D Plots
   • Animated Plots

Detailed Medium Article:

Detailed Source Code: Day 10 Commit

LinkedIn post:Day 10 Update

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DAY 11 (29 Sept 2023):

Topic: Data Cleaning on Loan Defaulter Dataset

1. Data Inspection.
2. Handling missing values.
3. Data Imputation

Detailed Source Code: Day 11 Commit

LinkedIn post: Day 11 Update

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DAY 12 (30 Sept 2023):

Topic: Data Visualization on Loan Defaulter Dataset

1. Binning of data for better visualizaiton
2. Univariant analysis
3. Bivariant analsis

Detailed Source Code: Day 12 Commit

LinkedIn post: Day 12 Update

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DAY 13 (1 Oct 2023):

Topic: Exploratory Data Analysis and Insights on Loan Defaulter Dataset

1. Finding insights from the visualizations

Detailed Source Code: Day 13 Commit

LinkedIn post: Day 13 Update

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DAY 14 (2 Oct 2023):

Topic: Descriptive Statistice

1. Mean, Median, Mode: These are measures of central tendency.
2. Variance and Standard Deviation: These quantify data spread or dispersion.
3. Skewness and Kurtosis: These describe the shape of data distributions.
4. Quantiles and Percentiles: These help analyze data distribution.
5. Box Plots for Descriptive Stats: Box plots provide a visual summary of the dataset.
6. Interquartile Range (IQR): The IQR is the range covered by the middle 50% of the data

Detailed Source Code: Day 14 Commit

LinkedIn post: Day 14 Update

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DAY 15 (3 Oct 2023):

Topic: Probability for Data Science

1. Probability Basics: Understand the fundamental concepts like events, outcomes, and sample spaces.
2. Probability Formulas: Master key formulas:
   • Probability of an Event (P(A)): Number of favorable outcomes / Total number of outcomes.
   • Conditional Probability (P(A|B)): Probability of A given that B has occurred.
   • Bayes' Theorem: A powerful tool for updating probabilities based on new evidence.
   • Law of Large Numbers: As you increase the sample size, the sample mean converges to the population mean. Crucial for statistical inference.
3. Probability Distributions: Get acquainted with probability distributions:
   • Normal Distribution: The bell curve is everywhere in data science. It's essential for hypothesis testing and confidence intervals.
   • Bernoulli Distribution: For binary outcomes (like success or failure).
   • Binomial Distribution: When dealing with a fixed number of independent Bernoulli trials.
   • Poisson Distribution: Used for rare events, like customer arrivals at a store.

Detailed Source Code: Day 15 Commit

LinkedIn post: Day 15 Update

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DAY 16 (4 Oct 2023):

Topic: Inferential Statistics

1. Central Limit Theorm
2. Hypothesis Testing
3. Deriving p-values
4. Z-Test
5. T-Test

Detailed Source Code: Day 16 Commit

LinkedIn post: Day 16 Update

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DAY 17 (5 Oct 2023):

Topic: Inferential Statistics

1. Chi-Square Test
2. F-Test/ANOVA
3. Covariance
4. Pearson Correlation
5. Spearman Rank Correlation

Detailed Source Code: Day 17 Commit

LinkedIn post: Day 17 Update

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DAY 18 (6 Oct 2023):

Topic: Introduction to Machine Learning

1. What is Machine Learning?
2. Types of Machine Learning?
3. Supervised Machine Learning
4. Unsupervised Machien Learning
5. Reinforcement Learning
6. Semi-supervised Learning

Detailed Source Code: Day 18 Commit

LinkedIn post: Day 18 Update

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DAY 19 (7 Oct 2023):

Topic: Steps in Machine Learning Project

1. Data Collection
2. Data Cleaning
3. Exploratory Data Analysis
4. Data Preprocessing
5. Data Splitting
6. Train the model
7. Evaluation of a Model
8. Deploy and Retrain

Detailed Source Code: Day 19 Commit

LinkedIn post: Day 19 Update

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DAY 20 (8 Oct 2023):

Topic: Exploring Scikit-Learn

1. sklearn.datasets
2. sklearn.preprocessing
3. sklearn.model_selection
4. sklearn.feature_selection
5. sklearn.linear_model And Many more...

Detailed Source Code: Day 20 Commit

LinkedIn post: Day 20 Update

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DAY 21 (9 Oct 2023):

Topic: Advanced Scikit-Learn Features

1. sklearn.metrics
2. sklearn.compose
3. sklearn.pipeline

Detailed Source Code: Day 21 Commit

LinkedIn post: Day 21 Update

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