Data Science And Machine Learning Projects — Mega Compilation Part 2
Part 2 …
Welcome back peeps. This post ( part 2 ) 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:Here we go —
Day 21: Advanced Regression Techniques with project ( Part 1)
In this post we covered Advanced Regression Techniques with a project.
Day 22: Advanced Regression Techniques with project ( Part 2)
In this post we covered Advanced Regression Techniques with a project
Day 23: Dimensionality Reduction using an Autoencoder in Python
Dimensionality is the number of input variables or features for a dataset and dimensionality reduction is the process through which we reduce the number of input variables in a dataset. A lot of input features makes predictive modeling a more challenging task.
Day 24: Support Vector Machine with a project
In this post we covered Support Vector Machine with a project
Day 25: Leave-One-Out Cross-Validation
It’s one of the technique in which we implement KFold cross-validation, where k is equal to n i.e the number of observations in the data. Thus, every single point will be used in a validation set, we will create n models, for n-observations in the data. Each point/sample is used once as a test set while the remaining data/samples form the training set.
Day 26: Scikit learn with a project
In this post we covered the basics of Scikit learn with a project.
Day 39: 60 days of Data Science and Machine Learning Series
Scikit learn with a project..
medium.com
Day 27: Tensorflow with a project
In this post we covered the basics of Tensorflow with a project.
Day 28 : Build Machine Learning Pipelines( With Code)
Pipeline is nothing but a technique through which we create linear sequence of data preparation and modeling steps to automate machine learning workflows. An automated pipeline consists of components and how those components can work together to produce and update the machine learning model.
Day 29: Regression using Tensorflow with a project
In this post we covered Regression using Tensorflow with a project.
Day 30: Classify Images of Clothing Using Tensorflow
Classification is a process of categorizing a given set of data into classes. The process starts with predicting the class of given data points where the classes can be referred to as target, label, or categories.
Day 31 : Neural Network with a project
In this post we covered the basics of Neural Network with Tensorflow with a project.
Day 32 : RNN and Tensorflow with a project
In this post we covered the basics of RNN and Tensorflow with a project.
Day 33 : Recurrent Neural Network with Keras
Recurrent Neural Networks (RNN) initially created in the 1980’s are a powerful and robust type of neural network in which output from the previous step are fed as input to the current step. The most important feature of RNN is Hidden state and they have memory which remembers each and every information through time.
Day 34: Recurrent Neural Network with a project
In this post we covered the basics of Recurrent Neural Network with a project
Day 35 : Custom Layers in Keras
Keras is a very powerful open source Python library which 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.
Part 3 of this series : Coming soon!
Follow for more updates. Stay tuned and keep coding!
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.
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.”
