Certified Machine Learning (Python) Professional

Course details

Machine learning has always been an important field in computer science, but recent advancements in computation power and algorithm efficiency have made this field more prominent than ever. Vskills certification on Machine Learning assesses the candidate as per the company’s need for Machine learning.

In this course, you will learn Supervised Learning, Supervised Learning Models, Unsupervised Learning, Neural Networks and Applying Machine Learning etc. You will also learn and explore the fundamental techniques in machine learning and discover how to solve many different types of problems using machine learning.

Note: You will receive an online access to e-learning (videos), hard copy material is not applicable to this course.

Why should one take Machine Learning Certification?
This Course is intended for professionals, freshers and graduates wanting to excel in their chosen areas. It is also well suited for those who are already working and would like to take certification for further career progression.

Earning Vskills Machine Learning Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential.

Who will benefit from taking Machine Learning Certification?
Job seekers looking to find employment in IT or software development departments of various companies, students who want to learn Machine Learning.
 
Benefits of Certification

• Government Certification
• Certification valid for life
• Lifelong e-learning access
• Learning Hours: 20+ hrs

 
How It Works

1. Select Certification & Register
2. Receive Online e-Learning Access (LMS)
3. Take exam online anywhere, anytime
4. Get certified & Increase Employability

Test Details

• Duration: 60 minutes
• No. of questions: 50
• Maximum marks: 50, Passing marks: 25 (50%).
• There is NO negative marking in this module.
• Online exam.

 
TABLE OF CONTENT

Machine Learning Foundations

• What is Machine Learning
• Types of Machine Learning
• How does a Machine Learning Algorithm Works
• Parametric and Non-Parametric Algorithms
• Regression and Classification
• Clustering
• Preparing Your Data
• Outliers
• Problem of Underfitting and Overfitting
• The Bias-Variance trade-off
• Intro to jupyter notebooks
• Data Science packages

Regression Analysis

• What is Regression Analysis
• Linear Regression
• Cost Function
• Gradient Descent
• Polynomial Regression
• Logistic Regression
• Cost Function for Logistic Regression
• Regularization
• Evaluating a Machine Learning Model

Bayesian Statistics

• Introduction to Conditional Probability
• Bayes Rule
• Bayesian Learning
• Naïve Bayes Algorithm
• Test your understanding of Bayes Theorem
• Solution – Test Your Understanding of Bayes Theorem
• Bayes Net
• Markov Chains

Tree Based Learning

• Decision Trees
• Gini Index
• ID3 Algorithm – Entropy
• ID3 Algorithm – Information Gain
• Practice Example – Information Gain

Project – 1

• House Price Predictions

Project – 2

• SMS Spam Classifier

Ensemble Learning

• What is Ensemble Learning
• Bagging
• Random Forest Algorithm
• Boosting

Support Vector Machines

• Introduction to Support Vector Machines
• Support Vectors
• Kernel
• Hyperparameters in SVMs

Instance Based Learning & Feature Engineering

• What is Instance Based Learning
• K-Nearest Neighbours Algorithm
• Dimensionality Reduction
• Principle Component Analysis
• Feature Scaling
• K-Means Algorithm

Project – 3

• Breast Cancer Prediction

Deep Learning

• Introduction to Deep Learning
• Perceptron
• Perceptron Exercise
• Solution – Perceptron Exercise
• Deep Neural Networks
• Deep Neural Networks – 2
• Activation Functions - 1
• Activation Functions - 2
• Backpropagation Algorithm
• Convolutional Neural Nets

Project – 4

• Image Classification using Deep Learning

Updated on 04 June, 2024