Data Science and ML Projects Series
Vertical series ( One post that will house all the projects as we build/implement them)
Welcome back peeps! Holiday season has started and before I jet off to my holidays, here’s something new that I have started which will help you build your Data science and ML skills through projects.
As we have already completed 60 days of Data Science and ML Series ; now we are moving ahead with the projects.
Projects Videos —
All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).
Subscribe today!
Tech Newsletter —
If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :
Goal/objective —
Note : Everyday new data science and ML projects will be uploaded/posted here. This is a vertical post so check this post regularly for new projects.
This post will house all the projects that you can build to have a solid projects portfolio, foundation and skills in Data science and Machine Learning. The goal is to develop an intuition and understand (in the depth) the practical side of data science and ML by building projects.
This post will not cover theory related to data science and Machine Learning. That you can cover through the pre-requisite.
I have created a GitHub repo for this series where we will be maintaining our code. Follow.
Pre-requisite to Data Science and ML projects —
Complete 60 days of Data Science and ML series ( as detailed below) before jumping on the projects —
Tools
We will be using Google Colabs/Jupyter Notebooks.
Through end to end projects, we will be covering —
1. Data Science
Data Collection — web scraping
Data Cleaning
Python
Pandas
Numpy
Summary Functions
Indexing
Grouping
Sorting
2. Analysis
Regression Analysis
Statistical Analysis
Least Square and inference
Missing Value Analysis
Fill the missing Values
Unique Value Analysis
Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Correlation Analysis
3. Data Visualization
4. Data Modeling
5. Data Evaluation
6. Math
Probability Distributions
Bayesian Inference
Statistics
7. Supervised Learning
Regression
Linear Regression
Ordinary Least Squares
Logistic Regression
Stepwise Regression
Multivariate Adaptive Regression Splines
Locally Estimated Scatterplot Smoothing
Classification
k-nearest neighbor
Support Vector Machines
Decision Trees
Ensemble Learning
Boosting
Stacking
Bagging
Random Forest
AdaBoost
8. Unsupervised Learning
Clustering
Hierchical clustering
k-means
Anomaly Detection
Density-based clustering
Fuzzy clustering
Mixture models
Dimension Reduction
Principal Component Analysis (PCA)
t-SNE; t-distributed Stochastic Neighbor Embedding
Factor Analysis
Latent Dirichlet Allocation (LDA)
Neural Networks
Self-organizing map
Adaptive resonance theory
Hidden Markov Models (HMM)
9. Semi-Supervised Learning
Clustering
Generative models
Low-density separation
Laplacian regularization
10. Scikit learn
Intro to scikit-learn
Scikit learn projects
11. Ensemble Modeling
12. Feature Engineering
Coming soon! Project 1 to 20.
Let me know if you have questions in the comment section below. Subscribe/ Follow, Like/Clap as it would encourage me to write more in my free time
Stay Tuned!!
Read More —
11 most important System Design Base Concepts
6. Networking, How Browsers work, Content Network Delivery ( CDN)
13. System Design Template — How to solve any System Design Question
System Design Case Studies — In Depth
Complete Data Structures and Algorithm Series
Some of the other best Series —
30 days of Data Structures and Algorithms and System Design Simplified
Data Science and Machine Learning Research ( papers) Simplified **
100 days : Your Data Science and Machine Learning Degree Series with projects
Complete Data Visualization and Pre-processing Series with projects
Exceptional Github Repos — Part 1
Exceptional Github Repos — Part 2
Tech Newsletter —
If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :
For Python Projects —
For complete 60 days of Data Science and ML : Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML
Follow for more updates. Stay tuned and keep coding!
For other projects, tune to —
Build Machine Learning Pipelines( With Code)
Recurrent Neural Network with Keras
Clustering Geolocation Data in Python using DBSCAN and K-Means
Facial Expression Recognition using Keras
Hyperparameter Tuning with Keras Tuner
Custom Layers in Keras
