Machine Learning and Introduction to Deep Learning

Whether you are a beginner or familiar with Machine Learning, this live course in ML covers fundamental concepts in a simple & easy way for high school students to understand. An introduction to Deep Learning is also included. So grab a seat, this summer at best prices.

Live Course

Live Class: Thursday, 07 Mar

Duration: 45 Hours

Enrolled: 53

Offered by: infyni

Client: NRIVA


Live Course
$150 50% off


About Course

Most of the material online on Machine Learning or Deep Learning skips mathematical and other very important concepts needed for high school students to understand applications of ML and Deep Learning.

This live course by expert instructors is delivered online and aims to make it simple and clear some of the fundamentals of ML and Deep Learning. Meant to ensure that any high schooler will understand it covers 30 hours of Machine Learning. 

Teaching machines how to learn or Machine Learning as an industry is expected to grow to nearly $31 billion in the next couple of years, say experts. So there is no better time than the Summer of 2023 to start preparing,

Our live course included assignments periodically. Assessments to check if the teaching is effective and includes real life scenarios. All classes are live, interactive so that you can clear your doubts on the spot with you instructor. 

Along with lifetime access to video recordings of your classes, all classes are monitored for quality control by an academic in charge. In addition infyni also conducts audits at random intervals with parents and students to know if their learning outcomes are met. 

To join the summer course in Machine Learning and take advantage of our best prices, enroll now. 

Course Offerings

  • Instructor Led Live sessions
  • Clarify doubts during session
  • Access Session Recordings
  • Attend on mobile and Tablet
  • Assessments and Competition
  • Direct Messages
  • Feedback from Instructor
  • Full lifetime Resources
  • Certificate of Completion
  • Topics
  • Instructor (1)
  • Reviews
  • Python
  • 12th Standard Maths 
  • Basics of Statistics
  • Random Variable
  • Data types
  • Qualitative and Quantitative Data
  • Measurement Scales
  • Population and Sample
  • Descriptive Statistics
  • Central Tendency
  • Variance
  • Data Visualization
  • Line Charts
  • Bar plots
  • Histograms
  • Box and Whisker Plots
  • Scatter Plots
  • Pie Charts
  • Area Plots 5. Probability Concepts
  • Permutations & Combinations
  • Probability Distributions
  • Central Limit Theorem
  • Normal Probability Distribution
  • Binomial Probability Distribution
  • Poisson Distribution
  • Inferential Statistics
  • Hypothesis tests
  • Types of errors
  • Test for Mean and Variance
  • One Sample Z test
  • Two Sample Z Test
  • One sample t test
  • Two Sample t Test
  • Chi Square Test
  • Correlation and Regression
  • ANOVA (Analysis of Variance)
  • Data Pre-processing & Exploratory Data Analysis
  • Regression a. Simple Linear Regression
  • Multiple Linear Regression
  • R2
  • Adjusted R2
  • Multi Collinearity & VIF
  • Understanding the Results
  • Fine Tuning the Model
  • SVR d. Decision Tree Regression
  • Random Forest Regression
  • Classification
  • Logistic Regression
  • Accuracy
  • Sensitivity
  • Specificity
  • Precision
  • Recall
  • ROC
  • AUC
  • Fine Tuning the Model
  • K-Nearest Neighbours (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Decision Tree Classification
  • Random Forest Classification
  • Bagging & Boosting
  • Dimensionality Reduction (Principal Component Analysis)
  • Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • k-Fold Cross Validation infyni
  • Introduction to Deep Learning
  • Artificial Neural Networks Fundamental
  • Convolutional Neural Network
  • Recurrent Neural Networks


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