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Our Modes of Training Approach
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Traditional Classroom Training led by our Instructors
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Exclusive Personalized Training tailored to your specific needs.
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Course Overview

Python Course Overview

The "Python Programming for Machine Learning" course will guide you through the essential features of Python for Machine Learning applications, complete with practical examples. You'll start by exploring the NumPy library, gaining proficiency in working with arrays, intersections, and differences, as well as loading and saving data during the initial section of the course.

The latter half delves into the Pandas library, focusing on its objects, data frames, and functions. By the end, you'll have the chance to test your knowledge with a quiz, allowing you to assess your progress and gain knowledge in Python for Machine Learning.

Osiz Labs offers Python for Machine Learning Course. This course offers an in-depth exploration of the core principles of Python programming for data science, machine learning, and AI. You'll learn about Python's data structures, functions, classes, file management, web scraping, data visualization, as well as machine learning and deep learning algorithms, among other key topics.

Python Course Skills You'll Gain

  • NumPy
  • Pandas
  • Supervised and unsupervised learning
  • Means clustering
  • Time series modeling
  • Naive Bayes
  • Deep Learning fundamentals
  • Random forest classifiers
  • Python
  • Machine Learning
  • Linear and logistic regression
  • Boosting and Bagging techniques
  • Kernel SVM
Skills You'll Gain
Course Introduction
Introduction to Machine Learning
Supervised Learning
Regression and Applications
Classification and Applications
Unsupervised Algorithms
Ensemble Learning
Recommender System
NumPy
Pandas

Our Python Course Modules

  • Course IntroductionCourse Introduction
  • introductionWhat You Will Learn
  • introductionIntroduction
  • introductionWhat is Machine Learning?
  • introduction Types of Machine Learning
  • introduction ML Pipeline and MLOP's
  • introduction Introduction to Python Packages Used in ML
  • IntroductionIntroduction
  • introductionSupervised Learning
  • introductionApplications of Supervised Learning
  • introductionPreparing and Shaping Data
  • introduction What are Overfitting and Underfitting?
  • introduction Detecting and Preventing Overfitting and Underfitting
  • introduction Regularization
  • IntroductionIntroduction
  • introductionWhat is Regression?
  • introductionRegression Types: Introduction
  • introductionLinear Regression
  • introduction Logistic Regression
  • introduction Polynomial Regression
  • introduction Ridge Regression
  • introduction LASSO Regression
  • IntroductionIntroduction
  • introductionWhat are Classification Algorithms?
  • introductionTypes of Classification
  • introductionTypes and selection of performance parameters
  • introduction Naive Bayes
  • introduction K Nearest Neighbors
  • introduction Decision Tree
  • introduction Random Forest
  • introduction Boruta Explained
  • introduction Support Vector Machine
  • IntroductionIntroduction
  • introductionWhat is Unsupervised Algorithms?
  • introductionTypes of Unsupervised Algorithms Clustering and Associative
  • introductionWhen to Use Unsupervised Algorithms?
  • introduction Independent Component Analysis
  • introduction BIRCH
  • IntroductionIntroduction
  • introductionWhat is Ensemble Learning?
  • introductionCategories in Ensemble Learning
  • introductionSequential Ensemble Technique
  • introduction Parallel Ensemble Technique
  • introduction Types of Ensemble Methods
  • introduction Reducing Errors with Ensembles
  • introduction Hello World Tensorflow
  • IntroductionIntroduction
  • introductionHow do recommendation engines work
  • introductionRecommendation Engine: Use Cases
  • introductionCollaborative Filtering and Memory-Based Modeling
  • introduction Item Based Collaborative Filtering
  • introduction User Based Collaborative Filtering
  • introduction Model-Based Collaborative Filtering
  • IntroductionJoining NumPy Arrays
  • introductionNumPy Intersection & Difference
  • introductionNumPy Array Mathematics
  • introductionSaving and Loading NumPy Array
  • IntroductionPandas Series Object
  • introductionIntroduction to Pandas Dataframe
  • introductionPandas Functions
  • introductionRecap
  • introduction Course Assessment

1.What You Will Learn
2.Course Introduction

Introduction
What is Machine Learning?
Types of Machine Learning
ML Pipeline and MLOP's
Introduction to Python Packages Used in ML

1.Introduction
2.Supervised Learning
3.Applications of Supervised Learning
4.Preparing and Shaping Data
5.What are Overfitting and Underfitting?
6.Regularization
7.Detecting and Preventing Overfitting and Underfitting

1. What are Classification Algorithms?
2.introductionTypes of Classification
3.introductionTypes and selection of performance parameters
4.introduction Naive Bayes
5.introduction K Nearest Neighbors
6.introduction Decision Tree
7.introduction Random Forest
8.introduction Boruta Explained
9.introduction Support Vector Machine

1.Introduction
2.What is Unsupervised Algorithms?
3.Types of Unsupervised Algorithms Clustering and Associative
4.When to Use Unsupervised Algorithms?
5.Independent Component Analysis
6.BIRCH

1.Introduction
2.Supervised Learning
3.Applications of Supervised Learning
4.Preparing and Shaping Data
5.What are Overfitting and Underfitting?
6.Regularization
7.Detecting and Preventing Overfitting and Underfitting

1.Introduction
2.What is Ensemble Learning?
3.Categories in Ensemble Learning
4.Sequential Ensemble Technique
5.Types of Ensemble Methods
6.Reducing Errors with Ensembles
7.Hello World Tensorflow

1.Introduction
2.How do recommendation engines work
3.Recommendation Engine: Use Cases
4.Collaborative Filtering and Memory-Based Modeling
5.Item Based Collaborative Filtering
6.User Based Collaborative Filtering
7.Model-Based Collaborative Filtering

1.Joining NumPy Arrays
2.NumPy Intersection & Difference
3.NumPy Array Mathematics
4.Saving and Loading NumPy Array

1.Pandas Series Object
2.Introduction to Pandas Dataframe
3.Pandas Functions
4.Recap
5.Course Assessment

What You Get From This Program

Eligibility
Eligibility

This course is not only suitable for beginners but also for individuals looking to switch career roles and enter the tech field. No prior programming experience is required, though a basic understanding of computers and logical thinking will help you navigate the course content effectively.

Certification
Certification

Upon successful completion, you will receive a globally recognized certificate, endorsed by NSDC (National Skill Development Corporation), to enhance your professional profile and career opportunities.

Career Guidance
Career Guidance

We provide valuable career guidance to help you take the next step in your professional journey. Our support includes resume building, interview preparation, and insights into industry trends, ensuring you're well-equipped to explore job opportunities in the field.

What You Get From This Program
Why Join This Program?

Why Join This Program?

For Career growth

Build skills for genuine career advancement with a cutting-edge curriculum crafted in collaboration with industry experts and academia to equip you with job-ready capabilities.

Professional Instructors

Build skills for genuine career advancement with a cutting-edge curriculum crafted in collaboration with industry experts and academia to equip you with job-ready capabilities.

Real-World Challenges

Gain practical experience by tackling real-world challenges through capstone projects that utilize real data sets and virtual labs for hands-on learning.

Learning Support

Benefit from continuous learning support with 24/7 access to mentors and a community of peers to help clear any conceptual uncertainties.

We Are Proud Of

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What is Unique about Osiz Labs?

  • Real-time project demo
  • Industry Experts as Trainers
  • Placement Support until your Dream Job Offer!
  • Free Interview Cracking Workshops
What Join This Program?
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Career and Guidence

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