Learn Machine Learning Course with Python A to Z

Name: Learn Machine Learning Course with Python A to Z
Rating: 4.2 (596 reviews)

Gain a solid understanding of machine learning concepts, algorithms, and applications in various fields.

Created byMaria EduCare

Last updated 2/2024

English

English [Auto],

What you'll learn

Understanding Machine Learning Language
Data Distribution
Bootstrap Aggregation
Cross Validation
Decision Tree
Hierarchical Clustering
Logistic Regression
Mean, Median, and Mode
Normal Data Distribution

Course content

1 section • 17 lectures • 2h 1m total length

Mean, Median, and Mode9:50
Learn how to calculate the mean, median, and mode in Python using a sample dataset. The tutorial builds simple code to compute and display these key statistics.
Percentiles7:16
This lecture shows how to compute percentiles in Python using NumPy, defining a function and applying it to two data sets to obtain the 75th and 5th percentiles.
Data Distribution6:34
Explore data distribution in Python by visualizing patterns with a histogram using NumPy and matplotlib, and generate a normal distribution to analyze frequency and central tendency.
Normal Data Distribution6:20
Explore the normal distribution, a bell-shaped, symmetric Gaussian curve, and learn to generate, visualize, and check for normality in Python using a mean, standard deviation, and a sample of 1000.
Multiple Regression6:09
Learn to model relationships with multiple regression using Python. Generate data with numpy, fit a linear model, and display coefficients and the intercept.
Scaling5:46
Apply minimum maximum scaling in Python to normalize features with different scales, using fit and transform to map data to a 0-1 range.
Train or Test6:41
Learn to implement train_test_split in Python with scikit-learn, evaluate model performance on unseen data, and generate training and testing sets using an 80/20 split.
Decision Tree7:17
Explore how to implement a decision tree classifier in Python using scikit-learn, iris data, and a train-test split to classify, predict, and evaluate accuracy for classification and regression tasks.
Confusion Matrix7:20
Explore how to interpret a confusion matrix in Python, evaluating a classification model with accuracy, precision, recall, and F1 score using a random forest and a heatmap.
Hierarchical Clustering9:02
Explore hierarchical clustering and its use in Python, showing how to generate random blobs, compute linkage metrics, and plot a dendrogram for agglomerative clustering.
Grid Search9:02
Explore grid search to optimize SVM hyperparameters in Python, using iris data, with grid search cross-validation to find best C, gamma, and kernel, achieving a test accuracy of 1.0.
Categorical Data6:09
Explore categorical data and learn to apply one-hot encoding in Python with pandas, converting a color column into a numerical encoded data frame.
K-Means Clustering8:00
Learn how k means clustering groups similar data in an unsupervised learning setup using Python, generating three cluster data, fitting a three-cluster model, and visualizing centers and labels.
K-nearest Neighbors4:42
Learn how to implement k-nearest neighbors in Python for classification (and regression) using the iris data set, including training, predicting, and evaluating accuracy with a k of 3.
Bootstrap Aggregation6:44
Explore bootstrap aggregation, or bagging, by training multiple decision tree classifiers on diverse data subsets and aggregating their predictions to boost iris dataset accuracy in Python.
Cross Validation5:53
Explore cross validation in Python by applying five-fold cross validation to an iris dataset with an SVM classifier, compute accuracy scores, and report the mean accuracy as a performance estimate.
Logistic Regression8:23
Explore logistic regression for binary classification in Python, including data creation, train-test split, model fitting, and evaluation with accuracy, confusion matrix, and classification report.

Requirements

No Programming Experience Needed

Description

Welcome to the "Learn Machine Learning Course with Python A to Z," your comprehensive guide to mastering the fascinating world of machine learning using Python. Whether you're an aspiring data scientist, software engineer, or business analyst, this course is meticulously crafted to take you on a journey from absolute beginner to proficient practitioner in machine learning.

Machine learning, a subset of artificial intelligence, has revolutionized countless industries by enabling computers to learn from data and make predictions or decisions without being explicitly programmed. Python, with its simplicity and powerful libraries, such as TensorFlow and scikit-learn, has become the go-to language for implementing machine learning algorithms.

Key Highlights:

Introduction to Machine Learning: Gain a solid understanding of machine learning concepts, algorithms, and applications in various fields.
Python Basics: Brush up on Python programming fundamentals necessary for implementing machine learning algorithms.
Data Preprocessing: Learn how to clean, preprocess, and prepare data for machine learning tasks to ensure accurate model training.
Supervised Learning: Explore supervised learning techniques, including linear regression, logistic regression, decision trees, and support vector machines.
Model Evaluation and Validation: Understand techniques for evaluating and validating machine learning models to ensure their reliability and effectiveness.
Deep Learning: Introduce yourself to deep learning concepts and neural networks using Python frameworks like TensorFlow and Keras.
Real-World Applications: Apply your machine learning knowledge to real-world projects and case studies across various domains, from healthcare to finance and beyond.

Why Choose This ?

Comprehensive Learning: This course covers machine learning from the basics to advanced topics, ensuring a thorough understanding of concepts and techniques.
Expert Instruction: Benefit from the guidance of experienced instructors passionate about machine learning and dedicated to your success.
Lifetime Access: Enroll once and enjoy lifetime access to course materials, allowing you to learn at your own pace and revisit concepts whenever necessary.
Career Opportunities: Machine learning expertise is in high demand across industries, making this course a valuable asset for career advancement and professional growth.

Embark on your journey to master machine learning with Python! Enroll now in "Learn Machine Learning Course with Python A to Z" and unlock the power of machine learning algorithms for data analysis, prediction, and decision-making.

Whether you're a beginner or have some experience in programming and data science, this course equips you with the knowledge and skills to thrive in the exciting field of machine learning. Don't miss this opportunity to elevate your career and become proficient in machine learning with Python!

Who this course is for:

Anyone Who Want to Learn Machine Learning