What you would learn in Machine Learning with Python (beginner to guru) course?
Machine learning is a subset of Artificial Intelligence (AI) that lets computers learn without being explicitly programmed. Machine learning is the development of software programs for computers that adapt to changes in information. In this article, we'll look at the basics of machine learning and how you can use Python to build an easy machine learning algorithm. Numerous modules have been developed through Python's Python community to help developers implement machine learning. The NumPy, SciPy, and scikit-learn modules are used during this class.
Machine learning is teaching a computer to use specific data sets and then using the training to anticipate the features of the data being received. Specialized algorithms are employed during the prediction and training phase, and the data from the training phase is fed to an algorithm that can then use the training data to make predictions based on new test data. Machine Learning (ML) is an area of computer science that allows computers to comprehend information precisely as humans can. In simple words, machine learning (ML) is a type of artificial intelligence that employs an algorithm or technique to discover patterns from raw data. The purpose of machine learning is to permit computer systems to be able to improve their experience without needing to be programmed or needing human intervention.
The Course's Objectives
Recognize the variety and depth of machine-learning applications and use cases that are real-world applications
You can import and unwind data using Python libraries before dividing it into test and training datasets.
Know Machine Learning concepts and types of ML.
Methods to prepare data, like multivariate and univariate analysis, missing values, outlier treatment, etc.
"Learn Machine" Learning algorithms: regression, classification, clustering, and association.
Use various classification techniques, including SVM Naive bayes, decision tree, random forest, etc.
Learn to interpret unsupervised learning and how to apply clustering algorithms
Implement polynomial and linear regression, comprehend Ridge and lasso regression and use various classification techniques like SVM Naive bayes, decision tree, and random forest
Avoidance of overfitting Minibatch, Bias-variance tradeoff, and Shuffling, ML tuning of solutions
Learn about different kinds of Recommender Systems and begin building your own!
Uplatz provides This comprehensive training in Machine Learning using Python programming.
You'll be able to understand the concept of machine learning and the most common techniques in the field at the end of this learning course. You'll be able to construct simple machine learning systems using Python through hands-on instruction. With the help of this Machine Learning course, you will learn to become proficient in Python and experience a gradual shift to data science. You will have a clear understanding of what machine learning is, what the different methods are, and what machine learning actually can accomplish. With this machine learning Python course, you will understand how to handle the latest technology.
Postgraduates, graduate students, and research students interested in the topic or enrolled in it as part of their education will benefit from this class. The student may be a beginner or an experienced student. The Machine Learning course has been designed to help both students and professionals become proficient quickly. This Machine Learning with Python training is a good starting point for the Machine Learning adventure.
Machine Learning with Python (beginner to expert) Course Program
1. A Brief Introduction to Machine Learning
What exactly is Machine Learning?
Need for Machine Learning
What is the reason, and when should Machines Learn?
The challenges in Machines Learning
Machine Learning and its Application Machine Learning
2. Different types of Machine Learning
Different types of Machine Learning
A) Supervised learning
B) Unsupervised learning
c) Reinforcement learning
Differentialities between Supervised and unsupervised learning
3. Components of the Python Components of ML Ecosystem
Utilizing pre-packaged Python Distribution Anaconda
4. Regression Analysis (Part-I)
Examples of Linear Regression
Scikit-learn library is used to implement simple linear regression
5. Regression Analysis (Part-II)
Multiple Linear Regression
Examples of Multiple Linear Regression
Examples of Polynomial Regression
6. Classification (Part-I)
What is Classification?
Classification Terms in Machine Learning
The types of learners that can be classified
An example of Logistic Regression
7. Classification (Part-II)
What exactly is KNN?
How is the KNN algorithm operate?
How do you determine how many neighbors are within KNN?
The implementation of the KNN classifier
What is a Decision Tree?
Implementation of the Decision Tree
SVM, as well as its application
8. Clustering (Part-I)
What is the purpose of Clustering?
Applications of Clustering
What exactly is K-Means? Clustering function?
K-Means Clustering algorithm examples
9. Clustering (Part-II)
Agglomerative Hierarchical Clustering and how it works
Awakening of Dendrogram in Hierarchical Clustering
The implementation of the Hierarchical Agglomerative clustering
10. Association Rule Learning
Association Rule Learning
The workings of the Apriori algorithm
The implementation of the Apriori algorithm
11. Recommender Systems
An Introduction to Recommender Systems
How does Content-based Filtering function
Implementation of the Movie Recommender Systems
- Explore the depths of Machine Learning (ML)
- Apply Python to Machine Learning programs
- Know what ML is, a necessity for ML, issues and the application of ML in real-life situations
- Different types of Machine Learning
- Components of the Python The components of the ML Ecosystem
- Anaconda, Jupyter Notebook, NumPy, Pandas, Scikit-learn
- Regression analysis
- scikit-learn Library that implements Simple Linear Regression
- Polynomial and Multiple Linear Regression
- Logistic Regression
- What is Classification, Classification? Terminologies for Machine Learning
- What exactly is KNN? What is the KNN algorithm function?
- What is a Decision Tree, and the implementation of the Decision Tree
- SVM and the way it is implemented
- What exactly is Clustering, and what are the applications of Clustering
- Clustering Algorithms
- K-Means Clustering and K Means Clustering algorithm examples
- Hierarchical Clustering
- Agglomerative Hierarchical Clustering and how it works
- The Woking Dendrogram in Hierarchical Clustering
- The implementation of the Hierarchical Agglomerative clustering
- Association Rule Learning
- Apriori algorithm and implementation of Apriori algorithm
- The Introduction Recommender Systems
- Content-based Filtering
- Collaborative Filtering
- Implementation of the Movie Recommender System