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School of Data Science Management and Technology

Call Now: +91 9830247087

Applied Machine learning and AI using Python

Interview sessions and Grooming Sessions for getting you ready for the topmost companies.

Duration
48 hours
1:1 Mentoring

Get ready for Interview

Internship

100% Assistance

Discount

Available

Contact Us

+91 9830247087

Get the Interview question bank and guidance from Industry Experts

Why Join Handson for this Course?

Through interactive exercises, you can learn and crack the most popular job-relevant interview questions.

Learn about Machine Learning, familiarize with all aspects of AI.

Learn to work with the versatile Python language for scripting Machine Learning applications and much more.

Master the various activities and methodology used by Artificial Intelligence Engineer.

Develop hands-on skills using the most popular Python libraries used by the ML Engineers.

Work with real-world datasets to enable stakeholders draw informed conclusions.

Program Overview

This Machine Learning and AI program includes both case study as well as grooming sessions. We cover critical topics on Python programming, Machine Learning algorithms, along with practical projects which helps in better understanding. This course deals with preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.

Key Features

Program Advantage

This Professional Certificate from Handson has a comprehensive course curriculum covering Statistics, Machine Learning algorithms, key Programming Languages and more – with a great detail via our interactive learning model so that you are comfortable with any kinds of questions asked in job-interview. Upon successfully completing this course, you will be able to fast track your career in the field of interest and it will help you to kick start into an exciting profession in AI and Machine Learning.

LEARNING PATH

  • What is Data Science?
  • Data Modeling and Visualization
  • Data Science Roles
  • Basics of Data Science
  • Challenges of Data Science
  • Business Use Cases for Data Science
  • Concept of Analytics and Statistics
  • Categories of Analytics
  • Properties of Measurement
  • Scales of Measurement
  • Concept of Data visualization
  • Measures of Central Tendency
  • Measures of Dispersion
  • Moments, Skewness and Kurtosis
  • Concept of Correlation and Covariance
  • Introduction to Probability Theory
  • Probability Distributions
  • Sampling and Estimation
  • Testing of Hypothesis
  • Basic Data types – Sets, Tuple, Lists, Dictionary
  • Packages – Pandas, Numpy, Matplotlib, Seaborn
  • Dataframe, series, array
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Kernel Regression
  • Evaluation Metrics
  • Sigmoid function
  • Mathematical Concepts of Logistic Regression
  • Binary and Multivariate Classification Problems
  • Lasso, Ridge, Elastic Net
  • K-Nearest Neighbors-Concept and Theory
  • Support Vector Machine(SVM)-Concept and Theory
  • Naïve Bayes Classifier- Concept
  • CART
  • Bagging, Boosting
  • Decision Tree Classifier-Concept
  • Random Forest Classifier-Concept
  • Evaluation Metrics
  • Dimensionality Reduction Problem- Curse of Dimensionality
  • Principal Component Analysis(PCA)
  • K-Means Clustering- Concept
  • K-Medoids
  • Hierarchical Clustering- Concept
  • DBSCAN Clustering-Concept
  • Dendogram
  • OPTICS, AGNES, DIANA
  • Evaluation metrics
  • Introduction of Deep Learning and Neural Network
  • Types and Applications of Neural Network
  • Skills required for Neural network
  • ANN and Neuron Structure
  • How does Neural Network Works?
  • ANN model Training
  • Activation Function
  • Fit all the Layers
  • Backpropagation
  • Image Reading and CNN Process
  • Steps of CNN
  • Conclusion of CNN Process
  • Applying CNN layers
  • Image Reading and CNN Process
  • Steps of CNN
  • Conclusion of CNN Process
  • Applying CNN layers

SKILLS COVERED

Get Your Dream Job

According to the Bureau of Labor Statistics, employment in the management field is projected to grow 11.5% from 2019 to 2029, faster than the average for all domains.

ADMISSION DETAILS

APPLICATION PROCESS

The application process consists of few simple steps. The  admission process will be completed after the payment is done by the selected candidates.

Submit Application

Submit the required documents for admission 

Application Review

Candidates will be selected based on their application

Admission

Selected candidates will get login credentials to begin the program

ELIGIBLE CANDIDATES

For admission to this program, candidates should have:

Any Graduate Candidate from any stream

Bachelor's degree with a minimum of 50% marks

Any aspiring Data Scientist in any functional area

ADMISSION GUIDENCE

We have a team, dedicated to solve your admissions related issues, who are available to guide you to apply for the program. They are available to:

Contact Us

+91 9830247087

ADMISSION FEE

We are dedicated to making our programs accessible and make it more economical.

Total Program Fee

₹ 18,000

(Incl. taxes)

For any fee related queries, please contact with our Admission Counselor in the above given number.

Apply Now

START APPLICATION
PROGRAM BENEFITS

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