Quick Recap —60 days of Data Science and Machine Learning
With Projects..
I hope you all are having a great weekend. In this post let’s recap what we have covered till now in our 60 days of Data Science and Machine Learning Series along with projects.
Some of the other best Series —
100 days : Your Data Science and Machine Learning Degree Series with projects
Complete Data Visualization and Pre-processing Series with 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).
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Day wise summary of what we have completed till now and Projects —
Day 1 : Python Basics with Code Implementation — Part 1
In this post we covered end to end Python Basics ( Part 1) that you should know. Topics like data types, strings, operators, and Chaining Comparison Operators with Logical Operators are covered.
Where to find Day 1 post :
Day 2: Python Basics with Code Implementation — Part 2
In this post we covered end to end Python Basics ( Part 2) that you should know. Topics like Python Lists and Dictionaries, Sets, Tuples etc are covered in detail.
Where to find Day 2 post :
Day 3: Python Basics with Code Implementation — Part 3
In this post we covered end to end Python Basics ( Part 3) that you should know. Topics like Tuples, Sets, Loops, Break and Continue Statements, Object-Oriented Programming and Class and attributes in Python are covered in detail.
Where to find Day 3 post :
Day 4: Intermediate Python with Code Implementation — Part 1
In this post we covered end to end Intermediate Python ( Part 1) that you should know. Topics like First Class functions, Private Variables, Global and Non Local Variables, __import__ function, Magic Functions, Tuple Unpacking, Static Variables and Methods in Python are covered in detail.
Where to find Day 4 post :
Day 5: Intermediate Python with Code Implementation — Part 2
In this post we covered end to end Intermediate Python( Part 2) that you should know. Topics like Lambda Functions, Magic methods, Inheritance and Polymorphism, Errors and Exception Handling, User-defined functions, Python garbage collection, and debugger are covered in detail.
Where to find Day 5 post :
Day 6 : Advanced Python with Code Implementation
In this post we covered end to end Advanced Python that you should know. Topics like Decorators, Memoization using Decorators, Generators, Ordered and Defaultdict, Coroutine with Code implementation are covered in detail.
Where to find Day 6 post :
Day 7– Statistics for Data Science and Machine Learning with Code Implementation
In this post we covered Statistics for Data Science you should know.
Where to find Day 7 post :
Day 8 — Maths for Data Science and Machine learning
In this post we covered Maths for ML . Topics like Linear Algebra, Calculus, Matrix and Vectors, Bayes Theorem and Cheatsheets etc are covered in detail.
Where to find Day 8 post :
Day 9 : Pandas Part 1 with Code Implementation
In this post we covered Pandas part 1 in depth with Code Implementation. Pandas is an open source Python package written for the Python programming language for data manipulation, analysis and ML tasks.
Where to find Day 9 post :
Day 10: Pandas Part 2 with Code Implementation
In this post we covered Pandas part 2 in depth with Code Implementation. Topics like indexing, filtering, transformation, Merging, Hierarchical Indexing etc are covered.
Where to find Day 10 post :
Day 11 : Numpy with Code Implementation
In this post we covered Numpy part 1 with focus on Flattening the arrays, Concatenation and Broadcasting etc in detail. Numpy is a python library for scientific computing — to work with multidimensional array objects and used to handle large amount of data. An array which is a grid of values and is indexed by a tuple of nonnegative integers is main data structure of the Numpy library.
Where to find Day 11 post :
Day 12 : Data Pre-processing Part 1 with Code Implementation
In this post we learned/implemented Hands on Data Pre-processing in depth — Part 1. Data preprocessing , one of the first and crucial step — the process in which we prepare the raw data and make it suitable for a ML model to increase its accuracy and efficiency.
Where to find Day 12 post :
Day 13 : Data Pre-processing Part 1 with Code Implementation
In this post we learned/implemented Hands on Data Pre-processing in depth — Part 2. Topics like Data Cleaning, Data Augmentation, Transformation, Channel Shift etc are covered in detail.
Where to find Day 13 post :
Day 14 : Regression Part 1 with Code Implementation
In this post where we learned/implemented Hands on Regression in depth — Part 1. Topics like Simple Linear Regression, Multi Linear Regression, Polynomial Regression are covered in detail.
Where to find Day 14 post :
Day 15 : Regression Part 2 with Code Implementation
In this post where we learned/implemented Hands on Regression in depth — Part 2. Topics like Support Vector Regression, Decision Tree Regression and Random Forest Regression are covered in detail.
Where to find Day 15 post :
Day 16 : Reflect and Connect the dots
In this we covered various Data Science and ML projects.
Where to find Day 16 post :
Day 16–60 days of Data Science and Machine Learning
Connect the docs..
medium.datadriveninvestor.com
Day 17 : Project — Kaggle’s annual Machine Learning and Data Science Survey ( Part 1 )
In this post we implemented a project and covered some of the most important concepts — data cleaning, preprocessing, EDA etc through a project.
You can get the dataset for this project from my Github repo(thanks to Kaggle ) —
This data ( Kaggle’s annual Machine Learning and Data Science Survey) has 42+ questions and 25,973 responses and for this post we will cover how to approach a problem and a very elementary view covering how to analyze your data.
Where to find Day 17 post :
Day 18 : Project —DecisionTreeRegressor and RandomForestRegressor
In this post we developed an intuition and implemented DecisionTreeRegressor and RandomForestRegressor through a project.
Where to find Day 18 post :
Day 19 : Project — Kaggle’s annual Machine Learning and Data Science Survey ( Part 2 )
In this post we covered second part of the Kaggle’s annual Machine Learning and Data Science Survey project.
Where to find Day 19 post :
Day 20: Project — Detailed Crypto Analysis (Part 1)
In this post we covered detailed Crypto Analysis to build a basic intuition and part 2 covers how we can build a model to predict the prices..
Where to find Day 20 post :
As we go forward we will be covering Scikit learn, Classification and Clustering problems, Keras, Tensorflow, Pytorch and most importantly all this will be covered with hands on projects.
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
That’s it fellas. Peace out and keep coding :)
Stay Tuned and of-course let me end this post with a quote by Avicii
Figure out what you’re most passionate about in life and what you’re good at. And the mixture between those two and then you should give it your all, all the time
