Machine Learning

Complete Overview Of The Course

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Topics Covered

Introducton To Machine Learning
  • Course Overview
  • Installation & Setup Process
Python Programming
  • Introduction to Numbers and Strings
  • Lists, Dictionaries, Tuples, Sets, and Booleans
  • Statements, Functions, and Operators in Python
  • Modules and Packages, Error, and Exception Handling
Introduction To AI/ML and Data Science
  • What is AI?
  • What is Machine Learning?
  • What is Data Science?
NumPy
  • Introduction to NumPy
  • Use Cases & Functions
Basics Of Pandas
  • Creation of DataFrames
  • CRUD Operations
  • Functions & Uses of Pandas
Data Visualization
  • Data Visualization with Matplotlib and Seaborn
Exploratory Data Analysis and Pre-Processing

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Linear Regression
  • Introduction to Polynomial Regression
  • Code Implementation in Python
Regression using Scikit-Learning
  • Implementation of Various Regression Models
  • Addressing Overfitting with Lasso and Ridge
Introduction To Classification And KNN
  • Understanding K-Nearest Neighbors (KNN)
  • Code Implementation
Logistic Regression And Naive Bayes
  • Theories and Mathematics behind Logistic Regression and Naive Bayes
  • Code Implementation
SVM And Regression Tree, Random Forest
  • Theories and Mathematics behind Logistic Regression and Naive Bayes
  • Code Implementation
Polynomial Regression
  • Introduction to Polynomial Regression
  • Code Implementation in Python
Unsupervised Learning
  • Introduction to Key Concepts in Unsupervised Learning
  • k-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
Introduction To Neural Networks
  • An Introduction to Neural Networks
  • The Foundation of Modern AI

Important Course Highlights

  • Pre – Recorded Content.
  • Live Group Q&A Session.
  • Course Community.
  • Unlimited Lifetime Access.
  • Internship Opprtunity.
  • Certificate Of Completion.
  • 00Hours
  • 00Minutes
  • 00Seconds

Course Description

Our Machine Learning course is designed to take you from the basics to advanced concepts, giving you the expertise needed to work confidently with real-world data. Whether you want to begin a career in machine learning, strengthen your data science skills, or use modern algorithms to address challenging problems, this program offers a comprehensive and hands-on learning experience. Stay future-ready with our curriculum that reflects the latest industry trends and practices.

Course Content

Why Choose Us?

What You Will Learn By This Course

Machine Learning

Machine Learning

Deep Learning

Deep Learning

Data Collection

Data Collection

Certificates

Our Mentors

Our Programs

How Can We Help You

What are the prerequisites for enrolling in the Machine Learning Course?

Basic programming knowledge in Python and a fundamental understanding of mathematics, particularly linear algebra and calculus, are recommended. No advanced prerequisites are required.

Who should take this machine learning course?

This course is ideal for beginners wanting to enter the field of machine learning, data enthusiasts looking to deepen their knowledge, career changers transitioning into data science, and professionals seeking to enhance their skills in machine learning.

What topics are covered in machine leaning course?

Topics include Python programming, data manipulation with NumPy and Pandas, data visualization, regression models, classification algorithms, clustering, neural networks, and real-world applications of machine learning.

How long is this course

The course typically spans 6-8 weeks, depending on your pace, including lectures, assignments, and hands-on projects.

Will I recieve a certificate on completion of this course?

Yes, you will receive a certificate of completion and a Letter of Recommendation upon successfully finishing the course and all required assignments and projects.