Pinned Repositories
2020-PY-101
This Python for beginners training course leads the students from the basics of writing and running Python scripts to more advanced features such as file operations, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting.
basics
📚 Learn ML with clean code, simplified math and illustrative visuals.
CodingNinjas_DataScience_MachineLearning
The notebooks are written in a way that they are sufficient on their own to learn the basics of Python, Machine Learning and Data Science.
data-science-complete-tutorial
For extensive instructor led learning
DeepLearningZeroToAll
TensorFlow Basic Tutorial Labs
DS_and_ML_projects
Data Science & Machine Learning projects and tutorials in python from beginner to advanced level.
machine-learning-for-software-engineers
A complete daily plan for studying to become a machine learning engineer.
machine-learning-for-trading
Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading
Machine-Learning-in-90-days
Machine-Learning-with-Python-1
Python code for common Machine Learning Algorithms
shrikant9793's Repositories
shrikant9793/data-science-complete-tutorial
For extensive instructor led learning
shrikant9793/PRML
PRML algorithms implemented in Python
shrikant9793/Pandas-Practice
Pandas
shrikant9793/Matplotlib
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural "pylab" interface based on a state machine, designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of Matplotlib.
shrikant9793/Numpy-with-Python
NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and random number capabilities
shrikant9793/Machine-Learning-with-Python-1
Python code for common Machine Learning Algorithms
shrikant9793/Data-Science-and-Machine-Learning-from-Scratch
Implements common data science methods and machine learning algorithms from scratch in python. Intuition and theory behind the algorithms is also discussed.
shrikant9793/Top-Deep-Learning
Top 200 deep learning Github repositories sorted by the number of stars.
shrikant9793/machine_learning
Basics of Machine learning end to end input data
shrikant9793/CodingNinjas_DataScience_MachineLearning
The notebooks are written in a way that they are sufficient on their own to learn the basics of Python, Machine Learning and Data Science.
shrikant9793/Hands-On-Machine-Learning-for-Algorithmic-Trading
Hands-On Machine Learning for Algorithmic Trading, published by Packt
shrikant9793/machine_learning_earthquake_rupture
This repository contains python codes to classify earthquake rupture based on random forest and neural network.
shrikant9793/ML_for_Hackers
Code accompanying the book "Machine Learning for Hackers"
shrikant9793/machine-learning-for-trading
Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading
shrikant9793/Python
Learn Python from Zero To Hero with free to learn
shrikant9793/Machine_Learning_2018
Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
shrikant9793/python-reference
Python Quick Reference
shrikant9793/Machine-Learning-101
Basics and findings of a range of different machine learning techniques. This includes Deep Learning, Computer Vision, Artificial Intelligence and Natural Language Processing (NLP).
shrikant9793/Pandas-Cookbook
Pandas Cookbook, published by Packt
shrikant9793/pandas_basics
basic pandas tutorials
shrikant9793/Machine-Learning-Models
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
shrikant9793/machine-learning-for-software-engineers
A complete daily plan for studying to become a machine learning engineer.
shrikant9793/machine-learning-dataschool
Detailed notes and code to learn the basics of machine learning with scikit-learn.
shrikant9793/Python-Machine-Learning-Cookbook
Code files for Python-Machine-Learning-Cookbook