COLLABORATION WITH


Machine Learning : With Python


Enrolled Students:- 150+
Resume Shortlisted:- 40+
Got Hired:- 10+
Life time access for customized Dashboard
Earn program completion and Internship completion certificate


Career Options

  1. Software Developer
  2. Data Scientist
  3. Data Analyst
  4. Computational Linguist

What will you learn?

  1. Basic Concepts of Python
  2. Learn to use Comments-Indentation, defining Functions and Error Handling
  3. Introduction to Statistics.
  4. Introduction to NumPy and Pandas
  5. Learn and Implement Machine Learning Algorithms
  6. Implement Linear Regression, Logistic Regression, Classification and Clustering

Skills Gained



  1. Mastery of programming languages like Python and relevant libraries (e.g., TensorFlow, PyTorch).
  2. Understanding of essential statistical concepts for data analysis and modeling.
  1. Knowledge of various machine learning algorithms and the ability to evaluate models using appropriate metrics.
  2. Proficiency in creating visualizations to convey insights and results from machine learning analyses.


PROGRAM LESSONS

Chapter 1. Introduction Python
  • 1.1-Python Crash course Introduction
  • 1.2 Python Demo n install
  • 1.3 Python Intro and Installation
  • 1.4 Basic python and datatype
  • 1.5 Basic,Number,string
  • 1.6 Data types
Chapter 2 - Control flow
  • 2.1 If else conditions
  • 2.2 While & for loop conditions
Chapter 3 - Exception Handling
  • 3.1 Exception Handling
Chapter 4 -Functions
Chapter 5 - OOPS
  • 5.1 CLASSES
  • 5.2 OOP
Chapter -6 Deep Learning
  • 6.1 Logistic Regression vs DL
  • 6.2 TesorFlow and Keras
Chapter -7 Libraries
  • 7.1 Introduction to Libraries
  • 7.2 Library Introduction
  • 7.3 Matplolib
  • 7.4 Numpy
  • 7.5 Pandas
Chapter 8 - Mathematics
  • 8.1 Data
  • 8.2 Linear Algebra
  • 8.3 Statistics
  • 8.4 Stats - Probs
Chapter 9 - Machine Learning Models
  • 9.1 Clustering
  • 9.2 Evaluation Metrics
  • 9.3 Logistic Regression - Feature Regression
  • 9.4 Logistic Regression
  • 9.5 Simple Linear regression
  • 9.6 Multiple Linear regression
Chapter 10 Intro to Probability & DV
  • 10.1 Introduction to Probability, Statistics & SQL
  • 10.2 Data Visualization with Tableau
  • 10.3 LSTM
Check Curriculum


PROJECT

Design
PCA for Customer Segmentation

PCA Segmentation: Employing PCA for customer grouping.

Content
Restaurant Review using NLP

NLP Restaurant Reviews: Analyzing reviews with NLP.


Design
Self Driving Car

Self-Driving Tech: Advancements in autonomous vehicles.

Content
Dogs VS Cats using CNN

CNN Cats & Dogs: Classification using CNN for pets.


Design
Lead Scoring Case Study

Lead Scoring Study: Exploring methods for lead evaluation.




PRICING PLANS

Standard Plan

Rs. 1999

+18% GST
  • Program Duration : 2 month
  • 2 Major Projects
  • 30+ Hours of content
  • Internship Offer Letter
  • Internship Completion Certificate
  • Program Completion Certficate
  • Fee is applicable only for the platform
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Student Choice

Rs. 2699

+18% GST
  • Program Duration : 3.5 months
  • 25+ Hours of Content
  • 4 Projects
  • Live Sessions During Project Execution & Training
  • Microsoft Certification
  • Internship Offer Letter
  • Internship Completion Certificate
  • Project Report
  • Fee is applicable only for the platform and Microsoft certification examination
Buy Now

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