COLLABORATION WITH

IBM: Machine Learning with Python

Duration: 2 Weeks
Price: Rs 6499 (+18%GST)

Exam Objective Register Now
IBM: Machine Learning with Python
  1. 1. Self Paced ~ 50 Hrs
  2. 2. IBM Catalyst Certificate on successful completion
  3. 3. Unlimited lifetime access to the course

Earning Potential
  1. 1. $101,730 on an average annually.
  2. 2. Prove that you have the ML skills it takes to build a better world. Earning your IBM: Machine Learning with Python can supply the foundation you need to build your career.
What will you learn?
  1. 1. Basic and Advanced Python
  2. 2. Concept of Statistics
  3. 3. Conceptualization and working of Machine Learning
  4. 4. Introduction to Statistics
  5. 5. Introduction to NumPy and Pandas
  6. 6. Capstone Project
Skills Gained
  1. 1. Gain knowledge of common ML workloads and how to implement them.
  2. 2. Associate or ML Engineer Associate
  3. 3. IBM: Machine Learning with Python can be used to prepare for other role-based certifications like Data Scientist

Our Pricing Plan

Rs 6,499

(+18%GST)

  • Self Paced
  • Microsoft Certificate
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Program Lessons

Machine Learning- Prerequisite

Chapter 1. Introduction Python - Pre - Learning Session
  • 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

Machine Learning with Python- IBM

Module 1 - Supervised vs Unsupervised Learning
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning
Module 2 - Supervised Learning I
  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees
Module 3 - Supervised Learning II
  • Regression Algorithms
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models
Module 4 - Unsupervised Learning
  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Measuring the Distances Between Clusters - Single Linkage Clustering
  • Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
  • Density-Based Clustering
Module 5 - Dimensionality Reduction & Collaborative Filtering
  • Dimensionality Reduction: Feature Extraction & Selection
  • Collaborative Filtering & Its Challenges

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