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Complete-Data-Science

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This repository contains everything you need to become proficient in Data Science

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# Complete Data Science with projects Series
This repository contains everything you need to become proficient in Data Science

![image](https://user-images.githubusercontent.com/93926742/144416831-ef6ad518-2963-403f-a13b-dba150f5e6ec.png)

Youtube for all the implemented projects and tech interview resources - [Ignito Youtube Channel](https://www.youtube.com/@ignito5917/about)

----------

**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: [Link](https://medium.datadriveninvestor.com/60-days-of-data-science-and-machine-learning-series-day-1-a62ce3c7aac1)

----------

**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 : [Link](https://medium.datadriveninvestor.com/day-2-60-days-of-data-science-and-machine-learning-series-5ef21f098454)

-----------

**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 : [Link](https://medium.datadriveninvestor.com/day-3-60-days-of-data-science-and-machine-learning-series-abcd9c6c5c18)

-----------

**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 :

Where to find Day 4 post : [Link](https://medium.datadriveninvestor.com/day-4-60-days-of-data-science-and-machine-learning-series-dd590f54d928)

-----------

**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 : [Link](https://medium.datadriveninvestor.com/day-5-60-days-of-data-science-and-machine-learning-series-f31259481904)

-----------

**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: [Link](https://medium.datadriveninvestor.com/day-6-60-days-of-data-science-and-machine-learning-series-3cfd04c1011c)

------------

**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 : [Link](https://medium.datadriveninvestor.com/day-7-60-days-of-data-science-and-machine-learning-6bc9cc2ceb0b)

------------

**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 : [Link](https://medium.datadriveninvestor.com/day-8-60-days-of-data-science-and-machine-learning-5155cfc78a68)

-----------

**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 : [Link](https://medium.datadriveninvestor.com/day-9-60-days-of-data-science-and-machine-learning-2d0f75a498b9)

----------

**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 : [Link](https://medium.datadriveninvestor.com/day-10-60-days-of-data-science-and-machine-learning-d5d789fbda79)

-----------

**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 : [Link](https://medium.datadriveninvestor.com/day-11-60-days-of-data-science-and-machine-learning-643f1b3e3659)

----------

**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 : [Link](https://medium.datadriveninvestor.com/day-12-60-days-of-data-science-and-machine-learning-1264f9c31b77)

----------

**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 : [Link](https://medium.datadriveninvestor.com/day-13-60-days-of-data-science-and-machine-learning-52df0d8d88f)

----------

**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 : [Link](https://medium.datadriveninvestor.com/day-14-60-days-of-data-science-and-machine-learning-7486395061b)

----------

**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 : [Link](https://medium.datadriveninvestor.com/day-15-60-days-of-data-science-and-machine-learning-65d582dc293a)

----------

**Day 16:Reflect and Connect the dots**

In this we covered various Data Science and ML projects .

Where to find Day 16 post : [Link](https://medium.datadriveninvestor.com/day-16-60-days-of-data-science-and-machine-learning-1e8340650e4)

-----------

**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. 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 : [Link](https://medium.datadriveninvestor.com/day-17-60-days-of-data-science-and-machine-learning-866400cab068)

-----------

**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: [Link](https://naina0412.medium.com/day-18-60-days-of-data-science-and-machine-learning-38c7a46f5496)

-----------

**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 : [Link](https://medium.datadriveninvestor.com/day-19-60-days-of-data-science-and-machine-learning-bba89212d7ff)

-----------

**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 : [Link](https://medium.com/coders-mojo/day-20-60-days-of-data-science-and-machine-learning-5ab13660b4ec)

-----------

**Day 21 : Exploratory Data Analysis Project**

Demonstrated how to do effective Exploratory Data Visualization.

Where to find Day 21 post : [Link](https://medium.datadriveninvestor.com/day-21-60-days-of-data-science-and-machine-learning-series-b0feb6ba71f4?sk=c2a68682a01ea2de48b837f429032db1)

----------

**Day 22 : All the Important ML algorithms with Projet 1**
  
Where to find Day 22 post : [Link](https://medium.com/coders-mojo/day-22-60-days-of-data-science-and-machine-learning-series-752dca7af6e?sk=aee759311db45f3fcb6f8b0e084b8a7f)

----------

**Day 23 : ML Classification and a Project.**

Where to find Day 23 post : [Link](https://medium.datadriveninvestor.com/day-23-60-days-of-data-science-and-machine-learning-series-242818479186?sk=499e1e658844c3ceb9c0e60ad7beb91c)

---------

**Day 24 : ML Classification Project 2 ( Part 1)**

Classification algorithms are used for predictive modeling problem where input training data is used to predict the probability that future data will fall into one of the predetermined/labelled categories.

Where to find Day 24 post : [Link](https://medium.com/coders-mojo/day-24-60-days-of-data-science-and-machine-learning-series-a151d4c86f51?sk=3d36f199bc940b844706108b02cb581d)

---------
**Day 25 : ML Classification Project 2 ( Part 2)**

Where to find Day 25 post : [Link](https://medium.com/coders-mojo/day-25-60-days-of-data-science-and-machine-learning-series-5aca25ae9756?sk=93f029610eb2d595f07c3912f0c4c807)

----------

**Day 26 : Machine Learning Clustering in detail with a project 1**

In this post we covered Machine Learning Clustering in detail with a project( Part 1).

Where to find Day 26 post : [Link](https://medium.datadriveninvestor.com/day-26-60-days-of-data-science-and-machine-learning-series-5d317cea4cad)

----------

**Day 27 : Machine Learning Clustering in detail with a project 1**

In this post we covered Machine Learning Clustering in detail with a project( Part 2).

Where to find Day 27 post : [Link](https://medium.datadriveninvestor.com/day-27-60-days-of-data-science-and-machine-learning-series-4c4b7fe6af7)

----------

**Day 28 : Machine Learning Clustering in detail with a project 2 ( part 1)**

In this post we covered Machine Learning Clustering in detail with another project( Part 1).
Where to find Day 28 post : [Link](https://medium.com/coders-mojo/day-28-60-days-of-data-science-and-machine-learning-series-ee7e4f3b6b46)

----------

**Day 29 : Machine Learning Clustering in detail with a project 2( part 2)**

In this post we covered Machine Learning Clustering in detail with another project( Part 2).

Where to find Day 29 post : [Link](https://medium.com/coders-mojo/day-29-60-days-of-data-science-and-machine-learning-series-a31184450ce5)

----------
**Day 30: Machine Learning Clustering in detail with a project 2 ( part 3)**

In this post we covered Machine Learning Clustering in detail with another project( Part 3).

Where to find Day 30 post : [Link](https://medium.com/coders-mojo/day-30-60-days-of-data-science-and-machine-learning-series-823fa9447928)

----------
**Day 31: Machine Learning Regression in detail with a project**

In this post we covered univariate linear regression with a project.

Where to find Day 31 post : [Link](https://medium.com/coders-mojo/day-31-60-days-of-data-science-and-machine-learning-series-7c211301bab0)

----------
**Day 32: Multiple linear regression with a project**

In this post we covered multiple linear regression with a project. Along the lines we evaluated model fit and accuracy using numerical measures such as R² and RMSE.

Where to find Day 32 post : [Link](https://medium.com/coders-mojo/day-32-60-days-of-data-science-and-machine-learning-series-c4a1205d37ff)

----------
**Day 33 : Logistic regression with a project**

In this post we covered logistic regression with a project.

Where to find Day 33 post : [Link](https://medium.com/coders-mojo/day-33-60-days-of-data-science-and-machine-learning-series-79830d11b365)

----------
**Day 34 : Logistic regression with another project**

In this post we covered logistic regression with another project .

Where to find Day 34 post : [Link](https://medium.com/coders-mojo/day-34-60-days-of-data-science-and-machine-learning-series-420df19d1ec0)

----------
**Day 35 : Principal Component Analysis with a project**

In this post we covered Principal Component Analysis with a project.

Where to find Day 35 post : [Link](https://medium.com/coders-mojo/day-35-60-days-of-data-science-and-machine-learning-series-63819382660)

----------
**Day 36 : Advanced Regression Techniques with project ( Part 1)**

In this post we covered Advanced Regression Techniques with a project

Where to find Day 36 post : [Link](https://medium.com/coders-mojo/day-36-60-days-of-data-science-and-machine-learning-series-7219a2bf77fc)

----------
**Day 37 : Advanced Regression Techniques with project ( Part 2)**

In this post we covered Advanced Regression Techniques with a project

Where to find Day 37 post : [Link](https://medium.com/coders-mojo/day-37-60-days-of-data-science-and-machine-learning-series-2e78afca9680)

----------
**Day 38 : Support Vector Machine with a project**

In this post we covered Support Vector Machine with a project

Where to find Day 38 post : [Link](https://medium.com/coders-mojo/day-38-60-days-of-data-science-and-machine-learning-series-6f9175b0d12)

----------
**Day 39 : Scikit learn with a project**

In this post we covered the basics of Scikit learn with a project.

Where to find Day 39 post : [Link](https://medium.com/coders-mojo/day-39-60-days-of-data-science-and-machine-learning-series-95af4ac9ac68)

----------
**Day 40 : Tensorflow with a project**

In this post we covered the basics of Tensorflow with a project.

Where to find Day 40 post : [Link](https://medium.com/coders-mojo/day-40-60-days-of-data-science-and-machine-learning-series-2f1214969836)

----------
**Day 41 : Neural Network with a project**

In this post we covered the basics of Neural Network with Tensorflow with a project.

Where to find Day 41 post : [Link](https://medium.com/coders-mojo/day-41-60-days-of-data-science-and-machine-learning-series-d0b6649587c9)

----------
**Day 42 : RNN and Tensorflow with a project**

In this post we covered the basics of RNN and Tensorflow with a project.

Where to find Day 42 post : [Link](https://medium.com/coders-mojo/day-42-60-days-of-data-science-and-machine-learning-series-d82a53d13cf7)

----------
**Day 43: Regression using Tensorflow with a project**

In this post we covered Regression using Tensorflow with a project

Where to find Day 43 post : [Link](https://medium.com/coders-mojo/day-43-60-days-of-data-science-and-machine-learning-series-299818452cea)

----------

**Day 44: Long Short Term Memory networks (LSTM) with Keras**

In this post we covered the basics of Long Short Term Memory networks (LSTM) with Keras through a project

Where to find Day 44 post : [Link](https://medium.com/coders-mojo/day-44-60-days-of-data-science-and-machine-learning-series-eee5568c4e97)

----------
**Day 45 : Recurrent Neural Network with a project**

In this post we covered the basics of Recurrent Neural Network with a project

Where to find Day 45 post : [Link](https://medium.com/coders-mojo/day-45-60-days-of-data-science-and-machine-learning-series-241136b9412e)

----------
**Day 46 : Language Classification with a project**

In this post we covered the basics of Multinomial Naive Bayes through a project.

Where to find Day 46 post : [Link](https://medium.com/coders-mojo/day-46-60-days-of-data-science-and-machine-learning-series-c7bbbb6750b2)

----------
**Day 47 : RNN and LSTM with a project**

In this post we covered the basics of RNN and LSTM with a project

Where to find Day 47 post : [Link](https://medium.com/coders-mojo/day-47-60-days-of-data-science-and-machine-learning-series-919df5d831db)

----------
**Day 48 : Multilayer Perceptron with project**

In this project we implemented a multilayer Perceptron model with Keras.

Where to find Day 48 post : [Link](https://medium.com/coders-mojo/day-48-60-days-of-data-science-and-machine-learning-series-b22b0c9bf384)

----------
**Day 49 : Yellowbrick for NLP**

In this post, we analyzed the text data using Yellowbrick and assess document similarity, topic modeling etc that are predicated on the notion of “similarity” between documents.

Where to find Day 49 post : [Link](https://medium.com/coders-mojo/day-49-60-days-of-data-science-and-machine-learning-series-311ab1d62bc2)

----------
**Day 50 : Bidirectional Encoder Representations from Transformers ( BERT) with a project**

In this post we learned how to fine tune BERT for text classification.

Where to find Day 50 post : [Link](https://medium.com/coders-mojo/day-50-60-days-of-data-science-and-machine-learning-series-33a30369d91a)

----------
**Day 51 : Yellowbrick with a project**

In this project we implemented visualization using yellowbrick

Where to find Day 51 post : [Link](https://medium.com/coders-mojo/day-51-60-days-of-data-science-and-machine-learning-series-b82a72fd1bd4)

----------
**Day 52 : Yellowbrick with 2nd project**

In this project we implemented visualization using yellowbrick through a project

Where to find Day 52 post : [Link](https://medium.com/coders-mojo/day-52-60-days-of-data-science-and-machine-learning-series-4e7788c3245e)

----------
**Day 53 : Yellowbrick with 3rd project**

In this project we implemented visualization using yellowbrick through a project

Where to find Day 53 post : [Link](https://medium.com/coders-mojo/day-53-60-days-of-data-science-and-machine-learning-series-d42724810a11)

----------
**Day 54 : Pytorch and ResNet with a project**

In this post we learned about the basics of PyTorch ( one of my favorite library) and ResNet.

Where to find Day 54 post : [Link](https://medium.com/coders-mojo/day-54-60-days-of-data-science-and-machine-learning-series-86491f964a0e)

----------
**Day 55 : Natural Language Processing using Naive Bayes through a project**

In this post we learned and implemented the basics of NLP using Naive Bayes through a project.

Where to find Day 55 post : [Link](https://medium.com/coders-mojo/day-55-60-days-of-data-science-and-machine-learning-series-7393ff714992)

----------
**Day 56 : ANN, Linear Regression, Decision Tree Regression and Random Forest with a project**

In this post we covered ANN, Linear Regression, Decision Tree Regression and Random Forest with a project

Where to find Day 56 post : [Link](https://medium.com/coders-mojo/day-56-60-days-of-data-science-and-machine-learning-series-71774a7fe5a1)

----------
**Day 57 : Deep learning and BERT**

In this post we learned how to perform sentiment analysis using BERT.

Where to find Day 57 post : [Link](https://medium.com/coders-mojo/day-57-60-days-of-data-science-and-machine-learning-series-43f3a687603c)

----------
**Day 58 : RNN and LSTM through a project**

In this post we covered the basics of RNN and LSTM through a project.

Where to find Day 58 post : [Link](https://medium.com/coders-mojo/day-58-60-days-of-data-science-and-machine-learning-series-2df3f4e03a55)

----------

**Day 59 : Natural Language Processing and Convolutions**

In this post we learned and implemented 1D Convolutions as Feature Extractors for Text in NLP.

Where to find Day 59 post : [Link](https://medium.com/coders-mojo/day-59-60-days-of-data-science-and-machine-learning-series-3786d513fcbd)

----------

**Day 60 : Transfer learning and Text Classification**

In this project we learned and implemented how to use transfer learning to fine-tune models, use pre-trained NLP text embedding models from TensorFlow Hub.

Where to find Day 60 post : [Link](https://medium.com/coders-mojo/day-60-60-days-of-data-science-and-machine-learning-series-29f72bd88c8c)

----------

# Some of the other best Series-

[Complete 60 Days of Data Science and Machine Learning Series ](https://medium.com/coders-mojo/day-1-day-60-quick-recap-of-60-days-of-data-science-and-ml-6fc021643d1?sk=4e75e043b7630a9f963562ebac94e129)

[30 days of Machine Learning Ops](https://medium.com/coders-mojo/day-1-of-30-days-of-machine-learning-ops-7c299e4b09be?sk=4ab48350a5c359fc157109e48b1d738f)

[30 Days of Natural Language Processing ( NLP) Series](https://medium.com/coders-mojo/quick-recap-30-days-of-natural-language-processing-nlp-with-projects-series-ceb674e3c09b?sk=ca09b27b3d5867f23ab4dc367b6c0c32)

[Data Science and Machine Learning Research ( papers) Simplified **](https://medium.com/coders-mojo/day-1-data-science-and-ml-research-papers-simplified-a68b00a3b1c4?sk=56136229ff738bd734f19d2b6953f78c)

[30 days of Data Engineering with projects Series](https://medium.com/coders-mojo/day-1-of-30-days-of-data-engineering-894822fcb128?sk=76ba558bfe2d9f85cbe741e505295531)

[60 days of Data Science and ML Series with projects](https://medium.com/coders-mojo/day-1-day-60-quick-recap-of-60-days-of-data-science-and-ml-6fc021643d1?sk=4e75e043b7630a9f963562ebac94e129)

[100 days : Your Data Science and Machine Learning Degree Series with projects](https://medium.com/coders-mojo/100-days-your-data-science-and-ml-degree-part-3-c621ecfdf711?sk=1a8c7b0c204d73432d56b7d1a3a26474)

[23 Data Science Techniques You Should Know](https://ai.plainenglish.io/23-data-science-techniques-you-should-know-61bc2c9d1b3a?sk=1680c36193eb22198974c9008d62a33c)

[Tech Interview Series — Curated List of coding questions](https://medium.com/coders-mojo/mega-post-tech-interview-the-only-list-of-questions-you-need-to-practice-ee349ea197bb?sk=fac3614684daff4b50a70c0a71e4d528)

[Complete System Design with most popular Questions Series](https://medium.com/coders-mojo/system-design-made-easy-quick-recap-of-complete-system-design-34af7e3aedfb?sk=bdd6a19edc1f3ce4a5064923f5b68721)

[Complete Data Visualization and Pre-processing Series with projects](https://medium.com/coders-mojo/complete-data-preprocessing-and-data-visualization-with-projects-mega-compilation-part-2-41584ef0920e?sk=842390da51689b8d43148c3980570db0)

[Complete Python Series with Projects](https://medium.com/coders-mojo/complete-python-and-projects-mega-compilation-7ec8f7adfe71?sk=ee0ecf43f23c6dd44dd35d984b3e5df4)

[Complete Advanced Python Series with Projects](https://medium.com/coders-mojo/complete-advanced-python-with-projects-mega-compilation-part-6-729c1826032b?sk=7faffe20f8039fa57099f7a372b6d665)

[Kaggle Best Notebooks that will teach you the most](https://medium.com/coders-mojo/my-list-of-kaggle-best-notebooks-topic-wise-data-science-and-machine-learning-part-2-84772863e9ae?sk=5ed02e419854a6c11add3ddc1e52947f)

[Complete Developers Guide to Git](https://medium.datadriveninvestor.com/the-complete-developers-guide-to-git-6a23125996e1?sk=e30479bbe713930ea93018e1a46d9185)

[Exceptional Github Repos — Part 1](https://medium.com/coders-mojo/6-exceptional-github-repos-for-all-developers-part-1-21e8fa04e150?sk=9140b249af6fe73d45717185fad48962)

[Exceptional Github Repos — Part 2](https://medium.com/coders-mojo/6-exceptional-github-repos-for-all-developers-part-2-3eec9a68c31c?sk=8e31d0eb7eb1d2d0bbbcecaa66bd4e7e)

[All the Data Science and Machine Learning Resources](https://medium.datadriveninvestor.com/best-resources-for-data-science-and-machine-learning-full-list-5ceb9a2791bf?sk=cf85b2cef95560c58509877a794577ff)

[210 Machine Learning Projects](https://medium.datadriveninvestor.com/210-machine-learning-projects-with-source-code-that-you-can-build-today-721b035649e0?sk=da5f593572a0261a6314afad99a0356c)

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# 6 Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) -

1. Complete Data Scientist : https://bit.ly/3wiIo8u

Learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud.

----

2. Complete Data Engineering : https://bit.ly/3A9oVs5

Learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets

-----

3. Complete Machine Learning Engineer : https://bit.ly/3Tir8ub

Learn advanced machine learning techniques and algorithms - including how to package and deploy your models to a production environment.

-----

4. Complete Data Product Manager : https://bit.ly/3QGUtwi

Leverage data to build products that deliver the right experiences, to the right users, at the right time. Lead the development of data-driven products that position businesses to win in their market.

------

5. Complete Natural Language Processing : https://bit.ly/3T7J8qY

Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more.

------

6. Complete Deep Learning: https://bit.ly/3T5ppIo

Learn to implement Neural Networks using the deep learning framework PyTorch

------

Complete-Data-Science

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