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Machine Learning Code Crash Course
Rating: 4.6 out of 5(28 ratings)
2,518 students

Machine Learning Code Crash Course

Coding for Machine Learning Algorithms in Python with Variety of datasets.
Created byNaman Singhal
Last updated 10/2023
English

What you'll learn

  • Machine Learning Algorithms
  • Coding
  • Supervised Learning & Unsupervised Learning
  • Data Visualization and Data Preprocessing

Course content

2 sections • 8 lectures • 50m total length

  • Linear Regression10:20

    Learn linear regression with a car price dataset, covering data loading, visualization, feature engineering, encoding, scaling, and train-test split. Evaluate performance with r-squared around 0.88 and analyze feature weights.

  • Logistic Regression6:47

    Explore logistic regression on diabetes data, perform data analysis with histograms and correlation, engineer features, split data, normalize with a standard scaler, and evaluate with accuracy, F1, and classification report.

  • Ridge and Lasso Regression6:09

    This crash course episode demonstrates ridge and lasso regression on Boston housing data, covering data prep, feature engineering, train-test split, scaling, and evaluating mean squared error and mean absolute error.

  • Decision Tree Classifier4:48

    Learn to build a decision tree classifier end to end: import libraries, explore the obesity classification dataset, engineer gender features, split data, train a decision tree, and achieve 100% accuracy.

  • Decision Tree Regressor6:41

    perform decision tree regression on a laptop dataset, using numpy, pandas, seaborn, and matplotlib for data prep, encoding, scaling, and achieving a mean squared error of 0.065.

  • Random Forest Classifier6:25

    Learn to apply the machine learning code crash course approach to a random forest classifier, covering data preprocessing, model training, accuracy evaluation, and feature importance visualization.

  • XGBOOST Classifier3:43

    Explore XGBoost, an ensemble learning technique, for multi-class drug prediction, including data preprocessing, encoding of categorical variables, and evaluating with accuracy scores.

Requirements

  • Maths behind Machine Learning

Description

Unlock the full potential of machine learning with our comprehensive Advanced Machine Learning Coding in Python course. Designed for both beginners and experienced developers, this course will take you on a deep dive into the world of machine learning and equip you with the skills and knowledge needed to excel in this rapidly evolving field.

In this hands-on course, you'll embark on a journey that starts with the fundamentals of machine learning and gradually progresses to advanced techniques and real-world applications. You will gain proficiency in Python, the primary programming language of choice for machine learning, and learn how to harness powerful libraries such as sci-kit-learn to build and train complex models.

I will guide you through a structured curriculum that covers topics like data preprocessing, feature engineering, model selection, hyperparameter tuning, and deploying machine learning models. You'll work on practical exercises, projects, and case studies, applying your newfound skills to solve real-world problems.

By the end of this course, you'll be well-prepared to tackle the most challenging machine-learning tasks, from image and text classification to regression and reinforcement learning. Whether you're aiming to advance your career, enhance your data analysis skills, or develop innovative machine-learning applications, this course provides the foundation you need. Join us and become a proficient machine-learning practitioner in Python!

Who this course is for:

  • Beginners in Machine Learning Coding

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