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Python for Machine Learning
The Python for Machine Learning certification emphasizes proficiency in using Python to implement machine learning algorithms. It covers fundamental concepts such as data manipulation, data analysis, visualization, and understanding various machine learning algorithms, how they function, and how to implement them using Python. This certification is sought by industries due to Python's flexibility and ease of use, making it an ideal choice for building scalable machine learning models. Industries leverage Python's robust libraries like NumPy, Pandas, Scikit-learn, etc., to parse vast datasets, gain actionable insights, predict future trends, improve decision-making, and enhance business operations.

Course Access

3 months

Last Updated

February 19, 2024

Students Enrolled

Total Video Time

5 hours, 20 minutes

Posted by

Machine Learning Scientistwith Python

Master the essential Python skills to land a job as a machine learning scientist! With this track, you'll gain a comprehensive introduction to machine learning in Python. You’ll augment your existing Python programming skill set with the tools needed to perform supervised, unsupervised, and deep learning. You'll learn how to process data for features, train your models, assess performance, and tune parameters for better performance. This track also covers topics including tree-based machine learning models, cluster analysis, preprocessing for machine learning, and more. By the time you finish, you’ll have the confidence to use Python for machine learning, working with real data sets, linear classifiers, gradient boosting, and more. In the process, you'll get an introduction to natural language processing, image processing, and popular Python machine learning packages such as scikit-learn, Spark, and Keras.

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Collins Chapusha
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Course Currilcum

    • Contents 00:05:00
    • Introduction 00:05:00
    • Getting Started in Python 00:30:00
    • Changing a Set in Python 00:05:00
    • Removing Items from Set 00:10:00
    • Syntax for Creating Functions with Argument 00:05:00
    • Scope of Variables 00:05:00
    • Default Argument 00:05:00
    • The *args Will Provide All Function Parameters in the Form of a tuple 00:10:00
    • Introduction to Machine Learning 00:05:00
    • History and Evolution 00:05:00
    • Different Forms 00:10:00
    • Diagnostic Analytics 00:10:00
    • Machine Learning Categories 00:10:00
    • Frameworks for Building Machine Learning Systems 00:10:00
    • Business Understanding 00:10:00
    • Data Analysis Packages 00:10:00
    • Data Structures 00:10:00
    • Basic Operations 00:10:00
    • Object Oriented 00:10:00
    • Machine Learning Core Libraries 00:05:00
    • Fundamentals of Machine Learning 00:05:00
    • Exploratory Data Analysis (EDA) 00:05:00
    • Machine Learning Perspective of Data 00:05:00
    • Supervised Learning– Regression 00:05:00
    • R-Squared for Goodness of Fit 00:05:00
    • Root Mean Squared Error (RMSE) 00:05:00
    • Multivariate Regression 00:05:00
    • Interpreting the OLS Regression Results 00:05:00
    • Outliers 00:05:00
    • ROC Curve 00:05:00
    • Regularization 00:05:00
    • Multiclass Logistic Regression 00:05:00
    • Load Data 00:05:00
    • Training Logistic Regression Model and Evaluating 00:05:00
    • Generalized Linear Models 00:05:00
    • Supervised Learning – Process Flow 00:05:00
    • Key Parameters 00:05:00
    • Components of Time Series 00:05:00
    • Unsupervised Learning Process Flow 00:05:00
    • Elbow Method 00:05:00
    • Average Silhouette Method 00:05:00
    • Model Diagnosis and Tuning 00:30:00

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