Tutorials
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- Pythonadd Python to PATH – How to add Python to the PATH environment variable in Windows?Adding Python to the PATH environment variable in Windows allows you to run Python commands from any directory within the command prompt
- Machine LearningMICE imputation – How to predict missing values using machine learning in PythonMICE Imputation, short for 'Multiple Imputation by Chained Equation' is an advanced missing data imputation technique that uses multiple iterations of Machine Learning model training to predict the missing values using known values from other features in the data as predictors.
- Machine Learningconda list environments – How to view all the virtual environments present in conda?In this one, let's see how to view a list of all virtual environments in conda. Conda is a popular package management system that allows you to create isolated environments .
- Machine Learningconda delete environment – How to remove a conda environment and all the associated packages?Let's see how to delete conda environment. If you no longer need an environment, you can delete it to free up disk space and simplify your development environment
- Machine LearningSpline Interpolation – How to find the polynomial curve to interpolate missing valuesSpline interpolation is a special type of interpolation where a piecewise lower order polynomial called spline is fitted to the datapoints.
- Machine LearningInterpolation in Python – How to interpolate missing data, formula and approachesInterpolation can be used to impute missing data. Let's see the formula and how to implement in Python.
- Machine LearningMissing Data Imputation Approaches | How to handle missing values in PythonThe quality of ML model results depend on the data provided. Missing values in data degrade the quality. Let's see how to use missing data imputation approaches to handle missing values.
- Machine LearningExploratory Data Analysis (EDA) – How to do EDA for Machine Learning Problems using PythonExploratory Data Analysis (EDA) is the step where you understand the data in detail: each variable through frequency counts and distribution plots, and the relationships between predictors and the response through scatterplots and correlations.
- Machine LearningML Modeling – Problem statement and Data descriptionML modeling is the step where machine learning is used to find patterns in data and use that learned knowledge to predict an outcome
- PythonHow to reduce the memory size of Pandas Data frameLet's see how to reduce the memory size of a pandas dataframe.
- Machine LearningAdaBoost – An Introduction to AdaBoostAdaboost is one of the earliest implementations of the boosting algorithm. It forms the base of other boosting algorithms.
- Machine LearningHow to formulate machine learning problemLet's understand how to define and formulate the machine learning problem (for predictive modeling) from a business problem.
- Machine LearningBuild your first ML projectBuild your first machine learning project in Python from scratch. A beginner-friendly, step-by-step guide, no prior ML experience needed to get started.
- PythonNumpy.random.randint() in pythonNumpy.random.randint is used to get random integers from low to high values. The low value is included while the high value is excluded while calculating.
- PythonHow to use numpy.random.uniform() in python.The numpy.random.uniform() function is used to create an array with random samples from a uniform probability distribution of given low and high values.
- PythonNumpy.sort() in pythonThe numpy.sort() function in python is used to sort the array along a specified axis.
- Pythonnumpy.median() – How to compute median in Pythonnumpy.median function is used to calculate the median of an array along a specific axis or multiple axes
- PythonHow to Use Numpy Where Function?The numpy.where() function is used to filter data based on the conditions provided. These conditions can vary.
- Machine LearningSetup Python environment for MLPython is the most popular programming language used for AI and machine learning. Let’s see how to setup python environment for ML using anaconda.
- Julia, Linux and careersLinux Commands List with ExamplesList of most useful Linux commands with description and examples
- Machine LearningTrain Test Split – How to split data into train and test for validating machine learning models?The train-test split technique is a way of evaluating the performance of machine learning models
- Julia, Linux and careersWhy learn from us?How is machinelearningplus university different from other courses that teach machine learning, Data Science , AI?
- Julia, Linux and careersWhat is a Data Scientist? – Roles, Responsibilities, Skillsets, Career Path and SalaryA Data scientist uses Data and AI to solve business problems, is skilled at working with data, extract meaningful insights, using ML to solve business problems, build applications that make predictions and recommendations, deploy and monitor the solutions.
- Julia, Linux and careersThe story of how Data Scientists came into existenceIn this one, I look back and reflect on how the profession of Data Scientists came into being.
- Machine LearningTask Checklist for Almost Any Machine Learning ProjectA cheat sheet of tasks and things to take care of for every end-to-end ML projects
- Machine LearningData Science Roadmap – How to become a Data Scientist? (6 month self study plan)The Data Science Roadmap: a self-study guide to machine learning in about 6 months, with what to learn for data science, ML, AI and statistics, the best resources for each topic, why each topic matters and a recommended study plan.
- Machine LearningWhy learn the math behind Machine Learning and AI?Why understanding the math behind ML algorithms matters even when libraries exist. Learn how mathematical intuition helps with debugging, tuning, and building better models.
- Machine LearningMistakes programmers make when starting machine learningI want to discuss some of the common mistakes that programmers make when starting to learn machine learning.
- Machine LearningMachine Learning Use Cases – The Big List of Real World Applications by Vertical and IndustryThe use cases of machine learning to real world problems keeps growing as ML/AI sees increased adoption across industries. Let's take of tour of the real world applications used by companies by verticals
- PythonHow to deal with Big Data in Python for ML Projects (100+ GB)?The size of the data you can load and train in your local computer is typically limited by the memory in your computer. Let's see how to deal with big data in python.