Machine Learning using Python Certification Course

Machine Learning using Python Certification course

Unleash data potential with machine learning with Python course

• Get career success with our comprehensive machine learning course
• Learn from 40+ hours of applied learning and interactive labs
• Complete 4 practical projects to strengthen your understanding
• Get mentoring support throughout your learning journey
• Master key ML concepts for certification
• Gain the skills needed to become a successful machine learning engineer

Offer: In addition to this practical e-learning course, we offer you free access to our online classroom sessions. You have 90 days to book free online training sessions, which always take place at flexible times. In addition to your e-learning and if you wish, you will have the opportunity to interact with the trainer and other participants. These online classroom sessions are also recorded, so you can save them.

Machine Learning Using Python Course Overview

This Machine Learning with Python course provides an in-depth overview of ML topics, including working with real-time data, developing supervised and unsupervised learning algorithms, regression, classification, and time series modeling. In this machine learning certification training, you will learn how to use Python to draw predictions from data.

Machine Learning Using Python - Key Features

• 30+ hours of blended learning
• 30+ assisted exercises and lesson-by-lesson knowledge checks
• Flexi Pass enabled: Ability to rebook your cohort within the first 90 days of access.
• 90 days of flexible access to online courses
• Live, online classroom training by top instructors and practitioners
• Lifetime access to self-paced learning content
• Industry-based projects for experiential learning
• Interactive learning with Google Colabs
• Dedicated live sessions by faculty with industry experts
• Practical skills and hands-on experience in applying machine learning to address real-world data challenges.

Skills covered

• Supervised and unsupervised learning
• Linear and logistic regression
• KMeans clustering
• Decision tree
• Boosting and Bagging techniques
• Time series modeling
• SVM with kernels
• Naive Bayes
• Random forest classifiers
• Basics of deep learning

Key learning outcomes

This machine learning course with Python will enable you to:

• Examine the different types of machine learning and their respective characteristics.
• Analyze the machine learning pipeline and understand the main operations involved in Machine Learning Operations (MLOps).
• Learn about supervised learning and its wide range of applications.
• Understand the concepts of overfitting and underfitting and use techniques to detect and prevent them.
• Analyze different regression models and their suitability for different scenarios.
• Identify linearity between variables and create correlation maps.
• List different types of classification algorithms and understand their specific applications.
• Master different types of unsupervised learning methods and when to use them.
• Gain a deep understanding of different clustering techniques in unsupervised learning.
• Investigate different ensemble modeling techniques, such as bagging, boosting and stacking.
• Evaluate and compare different machine learning frameworks, including TensorFlow and Keras.
• Build a recommendation engine using PyTorch
• Creating visualizations with Matplotlib, Seaborn, Plotly and Bokeh.

Pre-requisites

Learners need to possess an undergraduate degree or a high school diploma. An understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial. Before getting into the machine learning Python certification training, one should understand fundamental courses, including Python for data science, math refreshers, and statistics essential for data science.

Eligibility

The Machine Learning certification using Python course is well-suited for intermediate-level participants, including analytics managers, business analysts, information architects, developers looking to become machine learning engineers or data scientists, and graduates seeking a career in data science and machine learning.

Curriculum

1. Introduction to the course
2. Introduction to machine learning
3. Supervised learning
4. Regression and its applications
5. Classification and its applications
6. Algorithms for unsupervised learning
7. Ensemble learning
8. Recommended systems

Lesson 1: Introduction to the course

Get started with this program by understanding the course components and the topics covered. This will help you be prepared for the upcoming sessions.

Lesson 2: Introduction to Machine Learning

The course covers the basic concepts of machine learning, including its definition and different types. It also delves into the machine learning pipeline, MLOps, and AutoML, providing insights into deploying machine learning models at scale. In addition, students will be introduced to key Python packages for machine learning tasks, allowing them to leverage Python's robust ecosystem to develop machine learning solutions.

Topics covered:

• What is machine learning?
• Different types of machine learning
• Machine learning pipeline, MLOps and AutoML
• Introduction to Python packages for machine learning

Lesson 3: Supervised Learning

The section on supervised learning explores its practical applications in different fields and is accompanied by discussions on its relevance and importance in real-life scenarios. Students will engage in hands-on activities to prepare and shape data for supervised learning tasks, followed by discussions on overfitting and underfitting. In addition, practical exercises to detect and prevent these problems and insights into regularization techniques to optimize model performance and mitigate overfitting are provided.

Topics covered:

• Supervised learning
• Applications of supervised learning
• Overmatching and undermatching
• Regularization

Lesson 4: Regression and its Application

This segment delves into the basics of regression analysis, covering its definition and various types, including linear, logistic, polynomial, ridge, and lasso regression. Discussions highlight critical assumptions underlying linear regression and hands-on exercises provide practical experience in linear regression modeling. Participants also engage in data exploration using techniques such as SMOTE oversampling and prepare, build, and evaluate regression models to gain proficiency in regression analysis.

Topics covered:

• What is regression?
• Types of regression
• Linear regression
• Critical assumptions for linear regression
• Logistic regression
• Oversampling using SMOTE
• Polynomial regression
• Ridge regression
• Lasso regression

Lesson 5: Classification and its Applications

This session will cover classification algorithms and their definitions, types and applications, and the selection of performance parameters. Participants are immersed in various classification techniques, such as Naive Bayes, Stochastic Gradient Descent, K-Nearest Neighbors, Decision Trees, Random Forest, Boruta and Support Vector Machines, through discussions and assisted exercises. Key concepts such as Cohen's Kappa are also discussed, followed by knowledge checks to reinforce understanding.

Topics covered:

• What are classification algorithms?
• Different types of classification
• Application types and choice of performance parameters
• Naive Bayes
• Stochastic gradient descent
• K-nearest neighbors
• Decision tree Random Forest
• Boruta
• Support vector machine
• Cohen's cape

Lesson 6: Unsupervised Algorithms

This segment introduces students to unsupervised algorithms, covering their types, applications, and performance parameters. Participants engage in hands-on activities such as visualizing output and applying techniques such as hierarchical clustering, K-Means clustering, and the K-Medoids algorithm. In addition, they explore anomaly detection methods and dimensionality reduction techniques such as Principal Component Analysis (PCA), Singular Value Decomposition, and Independent Component Analysis. Practical applications of these algorithms are demonstrated through guided exercises, enhancing students' understanding of unsupervised learning concepts.

Topics covered:

• Unsupervised algorithms
• Different types of unsupervised algorithms
• When to use unsupervised algorithms?
• Parameters for performance
• Types of clustering
• K-Means clustering
• K-Medoids algorithm
• Outliers
• Detection of outliers
• Principal component analysis
• Correspondence analysis and multiple correspondence analysis (MCA)
• Singular value decomposition
• Independent component analysis
• Balanced iterative reduction and clustering using hierarchies (BIRCH)

Lesson 7: Ensemble Learning

In this section, we delve into ensemble learning techniques and explore sequential and parallel ensemble methods. Students discover different ensemble methods, such as bagging, boosting and stacking, along with their practical applications. Through guided exercises, participants gain hands-on experience in implementing ensemble techniques to reduce errors and improve model performance. In addition, they explore strategies such as averaging and max voting to further improve ensemble learning outcomes.

Subjects covered:

• Ensemble learning
• Sequential ensemble technique
• Parallel ensemble technique
• Different types of ensemble methods
• Bagging
• Boosting
• Stacking

Lesson 8: Recommendation Systems

This module provides a comprehensive overview of recommendation engines and explores their underlying principles and mechanisms. Participants are immersed in various use cases and examples of recommender systems and gain insights into their design and implementation. Through hands-on exercises, participants apply collaborative filtering techniques, including memory-based modeling, object-based and user-based filtering, and model-based collaborative filtering. In addition, they explore dimensionality reduction, matrix factorization methods, and accuracy matrices in machine learning to evaluate and optimize recommendation engine performance.

Subjects covered:

• How do recommendation engines work?
• Use cases for recommendation engines
• Examples of recommendation systems and how they are designed ¨
• Use PyTorch to build a recommendation engine.

Projects in industry

At the end of the course, you will do two projects. You will apply all your learnings and gain practical experience working with your new knowledge.

• Project 1: Staff turnover analysis - Create ML programs to predict staff turnover, including data quality checks, EDA, clustering, etc. and propose staff retention strategies based on probability scores.
• Project 2: Segmentation of Songs - Perform exploratory data analysis and cluster analysis to create cohorts of songs.

Bonus Courses!

Bonus 1: Math Refresher

• Probability and statistics
• Coordinate Geometry
• Linear Algebra
• Eingenvalues Eigenvectors and Eigendecomposition
• Introduction to Calculus

Bonus 2: Statistics Essential for Data Science

• Introduction to Statistics
• Understanding the Data
• Descriptive Statistics
• Data Visualization
• Probability
• Probability Distributions
• Sampling and Sampling Techniques
• Inferential Statistics
• Application of Inferential Statistics
• Relation between Variables
• Application of Statistics in Business
• Assisted Practice

Certificate

After completing this Machine Learning using Python course, you will receive a certificate, which will testify to your skills as a machine learning expert.