Machine Learning - Fundamental of Python Machine Learning

Name: Machine Learning - Fundamental of Python Machine Learning
Rating: 4.4 (727 reviews)

Learn The Most Effective Machine Learning Techniques in Python

Created bySara Academy

Last updated 11/2023

English

English [Auto],

What you'll learn

The Machine Learning Process
Standard Deviation
Linear Regression
Polynomial Regression
Multiple Regression
Hierarchical Clustering
Logistic Regression
Bootstrap Aggregation
Cross Validation

Course content

1 section • 22 lectures • 3h 19m total length

Mean, Median and Mode7:47
Explore mean, median, and mode as measures of central tendency and learn to compute them in Python using sorting, sum, and the statistics module.
Standard Deviation5:20
Learn how standard deviation measures data dispersion in Python by calculating variance and taking the square root to reveal data spread.
Percentiles8:54
Explore percentiles in data analysis by calculating 25th, median, and 75th percentiles in Python with NumPy, and visualize distributions with a Matplotlib box plot to show center and spread.
Data Distribution8:56
Explore data distribution, including normal, skewed, and uniform types, and how they inform machine learning decisions. Visualize with Matplotlib and NumPy, using histograms and statistics like mean and standard deviation.
Normal Data Distribution6:21
Explore the normal data distribution and its bell curve, and learn to generate and visualize it in Python with numpy and matplotlib, demonstrating mean and standard deviation using exam scores.
Scatter Plot7:57
Explore scatter plots as a visual gateway to relationships, correlations, and outliers in machine learning, using Python with matplotlib and seaborn to visualize feature distributions in iris data.
Linear Regression14:01
Explore linear regression in Python using scikit-learn to model relationships between variables within machine learning, fit a model, make predictions, visualize the regression line, and evaluate with mean squared error.
Polynomial Regression12:59
Explore polynomial regression to capture nonlinear relationships in data, implement polynomial features and linear regression in Python, and visualize the resulting curve.
Multiple Regression8:27
Explore multiple regression to model relationships between several predictors and an outcome, implemented in Python with sklearn, pandas, and linear regression, and evaluated using mean squared error.
Scale10:50
Learn how to apply feature scaling in Python using standard scaler to normalize features, improve model performance, and accurately predict house prices with linear regression.
Train or Test data9:43
Master the train-test split in Python to build models that generalize to unseen data, using 80/20 training and testing sets, and apply polynomial regression with polyfit and scatter plots.
Decision Tree9:56
Explore how decision trees model decisions in machine learning, using Python and scikit-learn to train on the iris dataset, split data, and evaluate with accuracy and a confusion matrix.
Confusion Matrix7:53
Explore how the confusion matrix reveals model performance beyond accuracy, detailing tp, tn, fp, fn, precision, recall, and f1 score, with Python, sklearn, and a random forest.
Hierarchical Clustering8:06
Explore unsupervised learning with hierarchical clustering in Python, where agglomerative merging forms clusters without predefining their number, using single, complete, and average linkage with a dendrogram.
Logistic Regression11:38
Explore binary classification with logistic regression in Python, learning how coefficients and intercept determine log odds and convert to probabilities.
Grid Search7:30
Apply grid search to tune hyperparameters in Python, using scikit-learn on datasets like iris to optimize logistic regression performance and compare parameter values.
Categorical Data9:49
Explore how to handle categorical data for machine learning. Use level encoding and one hot encoding in Python to convert categories like colors or January into numeric features.
K-Means13:14
Explore k-means clustering in Python by implementing the algorithm with scikit-learn, fitting three clusters to data, and visualizing the results with centroids and labels.
Bootstrap Aggregation6:59
Explore bootstrap aggregation, or bagging, to boost model stability and accuracy by training multiple decision trees on diverse data subsets, using bagging classifier with 50 estimators.
Cross Validation6:18
Learn cross validation for robust model validation in Python with an iris dataset example, using a k-nearest neighbors classifier and five-fold cross validation to evaluate performance.
AUC-ROC Curve11:17
Explore how the AUC-ROC curve evaluates a binary classifier's performance using Python and scikit-learn to train logistic regression, compute ROC metrics, and plot the curve.
K-nearest neighbors5:50
Explore the k-nearest neighbors algorithm and its Python implementation using the iris dataset, including a training-test split, a k value of three, and accuracy evaluation.

Requirements

No prior knowledge of Python Machine Learning is required

Description

Are you ready to learn on a journey into the captivating world of machine learning using Python? Welcome to "Machine Learning - Fundamentals of Python Machine Learning," your gateway to understanding and applying the core principles of machine learning.

Machine learning is transforming industries, from healthcare to finance, and Python is at the forefront of this revolution. Whether you're a budding data scientist, aspiring machine learning engineer, or simply curious about the potential of AI, this course will equip you with the foundational knowledge and practical skills to harness the power of Python for machine learning.

Key Learning Objectives:

Introduction to Machine Learning: Get a comprehensive overview of machine learning, its significance, and the Python ecosystem's role in the field.
Python for Machine Learning: Learn the basics of Python programming, data structures, and libraries essential for machine learning.
Model Evaluation and Selection: Discover techniques for evaluating machine learning models and selecting the best model for your tasks.
Feature Engineering: Master the art of feature selection and engineering to enhance the performance of your machine learning models.

Why Choose This Course?

Comprehensive Curriculum: This course is designed to take you from a machine learning novice to a proficient practitioner, ensuring you have a deep understanding of the fundamentals.
Hands On Learning: Practice your skills with coding exercises, hands-on projects, and machine learning challenges that replicate real-world scenarios.
Expert Instruction: Benefit from the guidance of experienced instructors who have worked on machine learning projects and are passionate about sharing their knowledge.
Lifetime Access: Enroll once and have lifetime access to the course materials, ensuring your skills stay up to date with the latest developments in machine learning.

Unlock the potential of Python in the world of machine learning. Enroll today in "Machine Learning - Fundamentals of Python Machine Learning" and acquire the knowledge and skills you need to excel in the exciting field of machine learning. Don't miss this opportunity to become a proficient machine learning practitioner.

Your journey to mastering machine learning with Python starts now!

Who this course is for:

Anyone interested in Machine Learning
Beginners Who want to Learn Machine Learning