Data Science And Machine Learning Projects — Mega Compilation Part 3
Part 3…
Welcome back peeps. This post( part 3) is all about Data Science and Machine Learning Projects that you can build to practically understand the concepts.
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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Part 1 of this mega series ( Day 0 — Day 20) can be found here —
Part 2 of this mega series ( Day 21— Day 35) can be found here —
Part 3:Here we go —
Day 36 : Hyperparameter Tuning with Keras Tuner
Hyperparameters are those tunable parameters which can directly affect how well a model trains and are set before the learning process begins.
Where to find Day 36 post :
Day 37 : Facial Expression Recognition using Keras
Keras is a very powerful open source Python library which is runs on top of top of other open source machine libraries like TensorFlow, Theano etc, used for developing and evaluating deep learning models and leverages various optimization techniques.
Where to find Day 37 post :
Day 38: 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 38 post :
Day 39: Language Classification with a project
In this post we covered the basics of Multinomial Naive Bayes through a project.
Where to find Day 39 post :
Day 40 : RNN and LSTM with a project
In this post we covered the basics of RNN and LSTM with a project
Where to find Day 40 post :
Day 41 : Analyzing Video using Python, OpenCV and NumPy
OpenCV first started at Intel by Gary Bradsky in 1999, is an open-source library which is used to develop real-time computer vision applications. With the main focus on image processing, video capture and analysis, when it is integrated with various libraries such as NumPy, python is capable of processing the OpenCV array structure for the analysis. In order to identify the patterns in the images as well as various other features we use vector space and perform mathematical operations.
Where to find Day 41 post :
Day 42 : Multilayer Perceptron with project
In this project we implemented a multilayer Perceptron model with Keras.
Where to find Day 42 post :
Day 43 : 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 43 post :
Day 44: Cluster Analysis using Python
Clustering is a technique of dividing the population or data points, grouping them into different clusters on the basis of similarity and dissimilarity between them. It helps in determining the intrinsic group among the unlabeled data points.
Where to find Day 44 post :
Day 45: 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 45 post :
Day 46: Yellowbrick with a project
In this project we implemented visualization using yellowbrick
Where to find Day 46 post :
Day 47 : Clustering Geolocation Data in Python using DBSCAN and K-Means
K-means clustering is a unsupervised ML technique which groups the unlabeled dataset into different clusters, used in clustering problems and can be summarized as —
i. Divide into number of cluster K
ii. Find the centroid of the current partition
iii. Calculate the distance each points to Centroids
iv. Group based on minimum distance
v. After re-grouping/re-allotting the points, find the new centroid of the new cluster.
Where to find Day 47 post :
Day 48: 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 48 post :
More Projects —
Complete Python And Projects — Mega Compilation
Complete Data Preprocessing and Data Visualization with Projects — Mega Compilation Part 2
Maths —
Statistics for Data Science and Machine Learning with Code Implementation
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.
Part 4 of this series : Coming soon!
For other projects, tune to —
Build Machine Learning Pipelines( With Code)
Recurrent Neural Network with Keras
Custom Layers in Keras
Follow for more updates, stay tuned and of-course let me end this post with a quote by Steve Jobs ;)
“Your time is limited, so don’t waste it living someone else’s life.”
