Bundle Multi (2-in-1) - Machine Learning

This is the Bundle Multi (2-in-1) – Machine Learning course by Uplatz consisting of self-paced training (pre-recorded) videos from two different instructors on the Machine Learning using Python. You will be awarded Course Completion Certificate at the end of the course.

Machine Learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information.

Machine Learning is a subset of AI that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own.

Features of Machine Learning

• It can learn from past data and improve automatically
• It is data-driven technology

Advantages of Machine Learning

Handling multi-dimensional and multi-variety data – Machine learning algorithms are good at handing data that are multi-dimensional and multi-variety in dynamic or uncertain environments.

Easy identifies trends and patterns - Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans.

The starting with simple 5 steps to perform Machine learning in Python

• Examine your problem
• Prepare your data
• Spot-check a set of algorithms
• Examine your results
• Double-down on the algorithms that worked best

Benefits of Python

Simplicity – Python is simple. It is extremely readable language. The code is clear and decipherable both for those who have written the strings and for those who are just random passerby.

Open Source Nature - Python is open-source. The ML calls for constantly growing and evolving use cases and the benefits of pythons open-sourceness allow customization according to the developers needs.

This Machine Learning with Python course by Uplatz is a complete and end-to-end course covering all topics of Machine Learning with Python programming in detail.

Bundle Multi (2-in-1) Machine Learning - Course Syllabus

Machine Learning with Python

Introduction :
1. Getting Started with Machine Learning
2. An Introduction to Machine Learning
3. What is Machine Learning ?
4. Introduction to Data in Machine Learning
5. Demystifying Machine Learning
6. ML – Applications
7. Best Python libraries for Machine Learning
8. Artificial Intelligence | An Introduction
9. Machine Learning and Artificial Intelligence
10. Difference between Machine learning and Artificial Intelligence
11. Agents in Artificial Intelligence
12. 10 Basic Machine Learning Interview Questions

Data and It’s Processing:
1. Introduction to Data in Machine Learning
2. Understanding Data Processing
3. Python | Create Test DataSets using Sklearn
4. Python | Generate test datasets for Machine learning
5. Python | Data Preprocessing in Python
6. Data Cleansing
7. Feature Scaling – Part 1
8. Feature Scaling – Part 2
9. Python | Label Encoding of datasets
10. Python | One Hot Encoding of datasets
11. Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python

Supervised learning :
1. Getting started with Classification
2. Basic Concept of Classification
3. Types of Regression Techniques
4. Classification vs Regression
5. ML | Types of Learning – Supervised Learning
6. Multiclass classification using scikit-learn
7. Gradient Descent :
• Gradient Descent algorithm and its variants
• Stochastic Gradient Descent (SGD)
• Mini-Batch Gradient Descent with Python
• Optimization techniques for Gradient Descent
• Introduction to Momentum-based Gradient Optimizer

8. Linear Regression :
• Introduction to Linear Regression
• Gradient Descent in Linear Regression
• Mathematical explanation for Linear Regression working
• Normal Equation in Linear Regression
• Linear Regression (Python Implementation)
• Simple Linear-Regression using R
• Univariate Linear Regression in Python
• Multiple Linear Regression using Python
• Multiple Linear Regression using R
• Locally weighted Linear Regression
• Python | Linear Regression using sklearn
• Linear Regression Using Tensorflow
• A Practical approach to Simple Linear Regression using R
• Linear Regression using PyTorch
• Pyspark | Linear regression using Apache MLlib
• ML | Boston Housing Kaggle Challenge with Linear Regression

9. Python | Implementation of Polynomial Regression

10. Softmax Regression using TensorFlow

11. Logistic Regression :
• Understanding Logistic Regression
• Why Logistic Regression in Classification ?
• Logistic Regression using Python
• Cost function in Logistic Regression
• Logistic Regression using Tensorflow

12. Naive Bayes Classifiers

13. Support Vector:
• Support Vector Machines(SVMs) in Python
• SVM Hyperparameter Tuning using GridSearchCV
• Support Vector Machines(SVMs) in R
• Using SVM to perform classification on a non-linear dataset

14. Decision Tree:
• Decision Tree
• Decision Tree Regression using sklearn
• Decision Tree Introduction with example
• Decision tree implementation using Python
• Decision Tree in Software Engineering

15. Random Forest:
• Random Forest Regression in Python
• Ensemble Classifier
• Voting Classifier using Sklearn
• Bagging classifier

Machine Learning (basic to advanced)

1. Introduction to Machine Learning
• What is a 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
• Supervised
• Unsupervised
• Reinforcement

3. Components of Python ML Ecco system
• Using Pre-packaged Python Distribution: Anaconda
• Jupyter Notebook
• NumPy
• Pandas
4. Regression Analysis (I)
• Regression Analysis
• Linear Regression
• Examples on Linear Regression
• scikit-learn library to implement simple linear regression

5. Regression Analysis (II)
• Multiple Linear Regression
• Examples on Multiple Linear Regression
• Polynomial Regression
• Examples on Polynomial Regression
•
6. Classification (I)
• What is Classification
• Classification Terminologies in Machine Learning
• Types of Learner in Classification
• Logistic Regression
• Example on Logistic Regression

7. Classification (II)
• What is KNN?
• How does the KNN algorithm work?
• How do you decide the number of neighbours in KNN?
• Implementation of KNN classifier
• What is a Decision Tree?
• Implementation of Decision Tree
•
8. Clustering (I)
• What is Clustering?
• Applications of Clustering
• Clustering Algorithms
• K-Means Clustering
• How does K-Means Clustering work?

9. Clustering (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

Everyone

Passion and determination to achieve your goals!

• Machine Learning Engineer
• Data Scientist
• Deep Learning Engineer
• AI Researcher
• Data Analyst
• Machine Learning Developer
• Data Consultant

Currently there are no Q&As for this course. Be the first to ask a question.

Uplatz Certificate of Completion

Digital certificate - Included