Subrat's Technical Blog

  Course Instructor: Fabio Mardero is a data scientist from Italy. He graduated in physics and statistical and actuarial sciences. He is currently working at a well-known Italian insurance company as a data scientist and Non-Life technical provisions evaluator.  Course Overview & Lectures Duration: 12+ hours Linear Algebra and Mathematical Foundation:   Maths For ML Series Part  1 Linear Algebra and Mathematical Foundation:  This course covers machine learning key elements, vector space, matrices, linear independence and basis and linear maps. Lecture 1:  01 01 Intro  (1 min) Lecture 2:  01 02 Machine learning Basics  (14 mins) Lecture 3:  01 03 Vector Spaces  (13 mins) Lecture 4:  01 04 Matrices  (31 mins) Lecture 5:  01 05 Linear Independence and Basis  (23 mins) Lecture 6:  01 06 Linear Maps  (40 mins) Total Time: ~ 2 hour Analytic Geometry:   Maths For ML Series Part  2 Analytic Geometry:  This course covers Lengths and Distances, Angles and Orthogonality, Orthogonal Projection

  The best way to learn a new programming language is to build projects with it. I have created a list of 25 beginner friendly project tutorials in Python. My advice for tutorials would be to watch the video, build the project, break it apart and rebuild it your own way. Experiment with adding new features or using different methods. That will test if you have really learned the concepts or not. You can click on any of the projects listed below to jump to that section of the article. If you are not familiar with the basics of Python, then I would suggest watching  this beginner freeCodeCamp Python tutorial . Python Projects You Can Build Mad Libs Guess the Number Game (computer) Guess the Number Game (user) Rock, paper, scissors Hangman Countdown Timer Password Generator QR code encoder / decoder Tic-Tac-Toe Tic-Tac-Toe AI Binary Search Minesweeper Sudoku Solver Photo manipulation in Python Markov Chain Text Composer Pong Snake Connect Four Tetris Online Multiplayer Game Web Scraping P

  In this article, I will take you through an explanation and implementation of all Machine Learning algorithms and models with Python programming language. All Machine Learning Algorithms & Models with Python Assumptions of Machine Learning Algorithms Mini-Batch K-Means Clustering Part of Speech Tagging Performance Evaluation Metrics Multinomial Naive Bayes Bernoulli Naive Bayes Agglomerative Clustering VisualKeras for Visualizing a Neural Network Stochastic Gradient Descent Explained Variance F-Beta Score Classification Report Passive Aggressive Regression R2 Score Lazy Predict Missing Values Calculation t-SNE Algorithm AutoKeras Tutorial Bias and Variance Perceptron Class Balancing Techniques One vs All & One vs One Polynomial Regression BIRCH Clustering Independent Component Analysis Kernel PCA Sparse PCA Non Negative Matrix Factorization Neural Networks Tutorial PyCaret Scikit-learn Tutorial NLTK Tutorial TextBlob Tutorial Streamlit Tutorial DBSCAN Clustering Naive Bayes P