Description

This certification course is designed to equip learners with the fundamental and advanced concepts of Machine Learning (ML). It covers theoretical foundations, practical implementations, and industry applications, enabling participants to build and deploy ML models effectively.

 

Skills you will get

 

Python (Internals, do’s and don’ts) Architecture, Data Structure

• Installation of Anaconda Prompt
• Jupyter Notebook-An Overview Shorcut Lkeys in Jupyter Notebook Data Types in Python

 

Python, Reading & Writing files in Python

• Rules for Naming the Variables List, Tuple, Set, Dictionary
• “Introduction to Files and directories
• Introduction to the command prompt or terminal paths” “Text files Reading from a text file Opening a file using `with'”

 

Loops and conditionals in python

• If, elif and else condition. For and While Loop

Data Analysis , Manipulation with numpy

• Machine Learning Libraries Numpy-Hands on

Pandas Python data science package to manipulate, calculate and analyze data

• “Pandas-Hands on”

 

Exploratory Data Visualization in Python with matploit

• Learn how to explore, visualize, and extract insights from data
• Data Visualization Matplotlib-Hands on Seaborn hands on

 

Statistical Thinking in Python (Part 1) Build the foundation

You need to think statistically and to speak the language of your data Measures of Central Tendency Measures of Dispersion “IQR Statistics-Hands-On”

Supervised Learning & Un-Supervised Learning

• Classification, Regression, Fine-tuning your model
• Supervised Learning
• Unsupervised Learning
• Linear Regression “Metrics in Linear Regression Hands-on in Linear Regression”

 

Logistic regression

• Logistic Regression
• Metrics in Logistic Regression
• Hands-on in Logistic Regression

Linear Regression

• Linear regression
• Metrics for Linear regression

 

Pre-processing for Machine Learning in Python

• Introduction to Data Preprocessing
• Standardizing Data
• Exploratory Data Analysis Missing Values Outliers “Standardization Mnormalization Feature Scaling and Selection”

 

Tree Based Models Classification and Regression Trees

• Decision Tree Bagging
• “Boosting Random Forest”

 

Fundamentals of Neural Network

• Neural Network

Project

• “Use data science packages, analysis, visualization, create model, extract pure data etc”

 

Internship or project

• The completion certificate for courses, internships, or projects will be issued only to those who meet the eligibility criteria.

Placement training

• Resume building – Professional & impactful CV
• Communication skills – Improve speaking & writing
• Interview prep – Ace personal interviews
• Group discussions – Stand out effectively
• Body language – Confidence & etiquette
• Job search tips – Find & apply smartly
• LinkedIn tips – Optimize for jobs