
Explore the basics of machine learning with Python, defining task, experience, and performance, and examine the need, challenges, and real-world data-driven applications.
Learn the three main types of machine learning—supervised, unsupervised, and reinforcement—along with semi-supervised, and see how supervised learning uses labeled data for regression and classification.
Explore the three core machine learning types—supervised, unsupervised, and reinforcement—plus semi-supervised learning. Learn clustering, association, regression, and classification with practical reinforcement examples.
Explore the components of the python machine learning ecosystem, emphasizing the Anaconda distribution and Jupyter notebook, and learn to install and use Anaconda Navigator and data science libraries.
Install and use the Python ML ecosystem components, including Anaconda, Jupyter notebooks, and NumPy; learn to launch notebooks, write and run code, and perform array operations.
Explore the Python machine learning ecosystem with pandas, install via Anaconda or pip, and use series, dataframes, and panels for data manipulation and analysis.
Explore the Python machine learning ecosystem with Anaconda distribution, Jupyter notebooks, NumPy, Pandas, and scikit-learn, covering installation and core algorithms like classification, regression, clustering, and dimensionality reduction.
Learn regression analysis as a supervised learning technique, focusing on linear regression. Understand the Y = mx + b relationship and predict a dependent variable from independent variable using Python.
Explore linear regression with a scatterplot and the line of best fit, interpreting slope, intercept, correlation (R), and P-value to predict future values, such as height to weight, using Python.
Build and evaluate a linear regression model linking fuel consumption to CO2 emissions. Use a training/testing split, scatterplots, and predictions with scikit-learn, then assess with MSE and R-squared.
Advance from simple linear regression to multiple linear regression and polynomial regression, predicting a dependent variable from independent variables. Grounded in supervised learning, explore regression examples like car CO2 emissions.
Learn to perform multiple linear regression to predict car CO2 emissions from weight and engine volume, build and fit a linear regression model, and interpret coefficients.
Master simple and multiple linear regression to predict car CO2 emissions from weight and volume, then apply polynomial regression for non-linear patterns. Assess fit with R-squared and visualize in Python.
Learn classification in machine learning, including binary, multiclass, and multilabel tasks, and lazy and eager learners, with a logistic regression example predicting university admission from GMAT, GPA, and work experience.
Apply logistic regression to predict university admission from GPA, work experience, and GMAT, splitting data into training and test sets, and evaluate with a confusion matrix and accuracy.
Explore classification concepts, including k-nearest neighbors, distance measures, and voting, with practical implementation and an example predicting playability from weather and temperature.
Extend classification by building a weather and temperature dataset, encoding categorical features with a label encoder, and training a Kinniburgh classifier to predict if you can play.
Explore how decision trees support classification and regression in supervised learning, and implement them in Python with scikit-learn to predict diabetes from BMI, glucose, and blood pressure.
Explore the support vector machine for classification, its linear kernel and maximum margin, and apply it to diabetes prediction using BMI and blood pressure features, highlighting accuracy.
Explore clustering in unsupervised learning, focusing on k-means clustering, how centroids and distances assign points, and common stopping criteria and applications.
Master k-means clustering from generating random two-dimensional data to training the model, identifying cluster centroids, visualizing with scatter plots, and predicting labels in Python.
Explore hierarchical clustering as an unsupervised learning method, comparing agglomerative and divisive approaches, and learn to interpret dendrograms and cluster distances.
This lecture demonstrates agglomerative hierarchical clustering to segment customers by shopping trends, guiding data import, feature selection (income and spending), choosing cluster numbers, and visualizing five clusters.
Explore association rule learning and market basket analysis, focusing on the apriori algorithm, its three measures—support, confidence, and lift—and Python-based implementation for transactional data.
Explore association rule learning and market basket analysis using the apriori algorithm, focusing on support, confidence, and lift through a dataset of 1000 transactions.
A warm welcome to the Machine Learning (beginner to guru) course by Uplatz.
Machine learning is a branch of artificial intelligence (AI) that allows computers to learn without having to be explicitly programmed. Machine learning is concerned with the creation of computer programs that can adapt to new data. In this post, we'll go through the fundamentals of machine learning and how to use Python to construct a simple machine learning algorithm. Many modules have been built by the Python community to assist programmers in implementing machine learning. The NumPy, SciPy, and scikit-learn modules will be used in this course.
Machine learning entails training a computer with a particular data set and then using that training to predict the characteristics of incoming data. Specialized algorithms are used in the training and prediction phase. The training data is sent into an algorithm, which then utilizes the training data to make predictions on fresh test data. Machine Learning (ML) is a branch of computer science that allows computers to make sense of data in the same manner that humans do. In simple terms, machine learning (ML) is a form of artificial intelligence that uses an algorithm or method to extract patterns from raw data. The goal of machine learning is to allow computers to learn from their experiences without having to be explicitly programmed or requiring human involvement.
Course Objectives
Recognize the range and depth of machine learning applications and use cases in real-world applications
Using Python libraries, import and wrangle data, then partition it into training and test datasets
Understand Machine Learning concepts and types of ML
Techniques for preparing data, such as univariate and multivariate analysis, missing values and outlier treatment, and so on
Learn Machine Learning algorithms - regression, classification, clustering, association
Implement various types of classification methods such as SVM, Naive bayes, decision tree, and random forest
Interpret unsupervised learning and learn to use clustering algorithms
Implement linear and polynomial regression, understand Ridge and lasso regression, and implement various types of classification methods such as SVM, Naive bayes, decision tree, and random forest
Overfitting avoidance, Bias-variance tradeoff, Minibatch, and Shuffling, ML solution tuning
Understand various types of Recommender Systems and start building your own!
Uplatz provides this end-to-end training on Machine Learning using Python programming.
You'll understand what machine learning is and what are the most prevalent approaches in the field are at the conclusion of this learning route. You'll be able to construct genuine machine learning systems in Python thanks to hands-on lessons. With this Machine Learning course you will become proficient in Python and will see a gradual transition to data science. You will gain a firm grasp of what machine learning is, what the various approaches are, and what machine learning can really do. With this machine learning python training, you can learn how to deal with this new technology.
Graduates, postgraduates, and research students who are interested in this subject or have it as part of their curriculum can benefit from this lesson. The reader may be a novice or a seasoned student. This Machine Learning course has been designed to help students and professionals get up to speed fast. The Machine Learning with Python training serves as a starting point for your Machine Learning adventure.
Machine Learning (beginner to guru) - Course Curriculum
1. Introduction to Machine Learning
What is Machine Learning?
Need for Machine Learning
Why & When to Make Machines Learn?
Challenges in Machines Learning
Application of Machine Learning
2. Types of Machine Learning
Types of Machine Learning
a) Supervised learning
b) Unsupervised learning
c) Reinforcement learning
Difference between Supervised and Unsupervised learning
Summary
3. Components of Python ML Ecosystem
Using Pre-packaged Python Distribution: Anaconda
Jupyter Notebook
NumPy
Pandas
Scikit-learn
4. Regression Analysis (Part-I)
Regression Analysis
Linear Regression
Examples on Linear Regression
scikit-learn library to implement simple linear regression
5. Regression Analysis (Part-II)
Multiple Linear Regression
Examples on Multiple Linear Regression
Polynomial Regression
Examples on Polynomial Regression
6. Classification (Part-I)
What is Classification
Classification Terminologies in Machine Learning
Types of Learner in Classification
Logistic Regression
Example on Logistic Regression
7. Classification (Part-II)
What is KNN?
How does the KNN algorithm work?
How do you decide the number of neighbors in KNN?
Implementation of KNN classifier
What is a Decision Tree?
Implementation of Decision Tree
SVM and its implementation
8. Clustering (Part-I)
What is Clustering?
Applications of Clustering
Clustering Algorithms
K-Means Clustering
How does K-Means Clustering work?
K-Means Clustering algorithm example
9. Clustering (Part-II)
Hierarchical Clustering
Agglomerative Hierarchical clustering and how does it work
Woking of Dendrogram in Hierarchical clustering
Implementation of Agglomerative Hierarchical Clustering
10. Association Rule Learning
Association Rule Learning
Apriori algorithm
Working of Apriori algorithm
Implementation of Apriori algorithm
11. Recommender Systems
Introduction to Recommender Systems
Content-based Filtering
How Content-based Filtering work
Collaborative Filtering
Implementation of Movie Recommender System