Goals of ML Problem ?

The goal of any machine learning problem is to find a single model that will best predict our wanted outcome. Rather than making one model and hoping this model is the best/most accurate predictor we can make, ensemble methods take a myriad of models into account, and average those models to produce one final model. It is important to note that Decision Trees are not the only form of ensemble methods, just the most popular and relevant in DataScience today.

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CondaValueError: Value error: invalid package specification

Recently I was trying to create Conda Environment and wanted to install Tensorflow but i have faced some issue , so i have done some research and done trouble shooting related to that . Here am going to share how to trouble shoot if you are getting Conda Value error while creating Conda environment and install tensorflow . Open Anaconda Prompt (as administrator if it was installed for all users) Run conda update conda Run the installer again Make sure all pkg are updated: Launch the console from Anaconda Navigator and conda create -n mypython python=3.6.8 After Installing Conda environment please active the conda now : conda activate mypython once conda environment has been activated kindly install tensorflow 2.0 by using this command pip install tensorflow==2.0.0 once Tensorflow has been successfully install kindly run the command : pip show tensorflow Try to Run Comman PIP Install Jupyter lab and after installing launch the

DataScience Mandatory skills for 2020

The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market . Data Science is a competitive field, and people are quickly building more and more skills and experience. This has given rise to the booming job description of Machine Learning Engineer, and therefore, my advice for 2020 is that all Data Scientists need to be developers as well. To stay competitive, make sure to prepare yourself for new ways of working that come with new tools. 1. Agile Agile is a method of organizing work that is already much used by dev teams. Data Science roles are filled more and more by people who’s original skillset is pure software development, and this gives rise to the role of Machine Learning Engineer.More and more, Data Scientists/Machine Learning Engineers are managed as developers: continuously making improvements to Mac

Math Skills required for Data Science Aspirants

The knowledge of this essential math is particularly important for newcomers arriving at data science from other professions, Specially whosoever wanted to transit their career in to Data Science field (Aspirant). Because mathematics is backbone of Data science , you must have knowledge to deal with data, behind any algorithm mathematics plays an important role. Here am going to iclude some of the topics which is Important if you dont have maths background. 1. Statistics and Probability 2. Calculus (Multivariable) 3. Linear Algebra 4. Methods for Optimization 5. Numerical Analysis 1. Statistics and Probability Statistics and Probability is used for visualization of features, data preprocessing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation, etc. Here are the topics you need to be familiar with: Mean, Median, Mode, Standard deviation/variance, Correlation coefficient and the covariance matrix, Probability distributions (Binomi

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