What you'll learn:
- Deep dive into the world of Machine Learning (ML)
- Apply Python for Machine Learning programs
- Understand what is ML, need for ML, challenges & application of ML in real-life scenarios
- Types of Machine Learning
- Components of Python ML Ecosystem
- Anaconda, Jupyter Notebook, NumPy, Pandas, Scikit-learn
- Regression analysis
- scikit-learn Library to implement Simple Linear Regression
- Multiple Linear Regression and Polynomial Regression
- Logistic Regression
- What is Classification, Classification Terminologies in Machine Learning
- What is KNN? How does the KNN algorithm work?
- What is a Decision Tree and Implementation of Decision Tree
- SVM and its implementation
- What is Clustering and Applications of Clustering
- Clustering Algorithms
- K-Means Clustering and K-Means Clustering algorithm example
- Hierarchical Clustering
- Agglomerative Hierarchical clustering and how does it work
- Woking of Dendrogram in Hierarchical clustering
- Implementation of Agglomerative Hierarchical Clustering
- Association Rule Learning
- Apriori algorithm and Implementation of Apriori algorithm
- Introduction to Recommender Systems
- Content-based Filtering
- Collaborative Filtering
- Implementation of Movie Recommender System
A warm welcome to the Machine Learning with Python 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 with Python (beginner to guru) - Course Syllabus
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
