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Machine Learning Course – Online

Learn ML from basics with live training sessions

Machine Learning Course – Online

Learn ML in the new normal way.

Predicting the future isn’t magic, it’s artificial intelligence!

Are you looking for the best Online Course on Machine Learning with trainer interaction?

We have made our Machine Learning Course available online now. This will help participants to learn from basics of Machine Learning, understand the math behind algorithms and get hands-on experience working on ML algorithms. This curriculum was developed in association with HyperVerge. At regular intervals, problem statements will be given for the participants to solve and gain more insights into algorithms.

The course will have two levels of training helping you to learn, work & specialize on ML algorithms.

Join now to learn ML & enhance your hands-on skills with our Online Classes.

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Course Highlights

  • Designed for working professionals, engineering students & faculties.
  • Online live classes with expert trainers capable of making learning-technology simple.
  • Work on over 15+ real-time data sets.
  • A seamless transition between theoretical concepts and practical hands-on.
  • Continuous Assessment and mentorship.

Skills you’ll learn

  • Python programming.
  • Clear understanding of ML algorithms & math behind them.
  • Data cleaning and Pre-Processing of numerical and text data.
  • Predictive Analytics and statistics.

Curriculum

Machine Learning Course – Level 1

Week 1: Linear Regression

– What is Artificial Intelligence
– Machine Learning & Types
– Fundamentals of Python
– Data Preprocessing in python
– Defining a Model
– Error Calculation
– Gradient Descent Algorithm

Problem: Predicting Housing Prices based on the size of the house using Gradient Descent Algorithm

Week 2: Logistics Regression (Classification)

– Classification problem
– Data Preprocessing
– Defining a Model
– One vs. All
– Error & Accuracy Calculation
– Gradient Descent Algorithm
– Prediction

Problem: Handwritten Digit Recognition using Gradient Descent Algorithm

Week 3: Multi variate Linear Regression

– Introduction to Scikit Learn
– Label Encoding
– Data Preprocessing
– Gradient Descent Algorithm, TNC
– Regularization Parameter
– Hyperparameter Grid Search
– Bias, Variance, Accuracy, Precision

Problem: Solve Kaggle Datasets

Week 4: K Nearest Neighbour

– What is KNN?
– Example KNN Problem
– Defining the Objective function
– Optimize Objective function
– Prediction & Accuracy

Problem: Online shoppers’ buying intention.

Week 5: Decision Tree & Random Forest

– What is Decision Tree?
– Calculating Entropy
– Calculating Information gain
– Gini Index
– Objective Function

– What is Random Forest & Bagging?
– Advantages of using Random Forest
– Pruning
– Objective Function
– Prediction & Accuracy

Decision Tree Problem: Depending on the weather predicting the possibility of playing cricket
Random Forest Problem: Behavioral Risk factor Surveillance System

Machine Learning Course – Level 2

Week 1: Naive Bayes

– NLP Basics
– N Gram Model, Bag of Words
– TF-IDF Vectorisation
– Bayes Theorem
– Multinomial & Bernoulli Naive Bayes
– SMOTE
– Prediction & Accuracy

Problem: Building a spam classifier

Week 2: Support Vector Machine (SVM)

– What is SVM?
– Intuition Behind SVM
– Defining the Objective function
– Optimize Objective function
– Kernels
– Prediction & Accuracy
– HOG SVM
– Sliding Window technique
– OpenCV
– Optimisation

Problem: Solving MNIST dataset, Facial Recognition

Week 3: Neural Networks

– What is Neural network?
– Defining a Model
– Back Propagation Algorithm
– Classification Problem

Problem: Solving Fashion MNIST dataset

Week 4: Unsupervised Learning

– K Means Clustering
– Hierarchical Clustering
– K Means for non-separated clusters
– Principle Component Analysis

Problem: Building a spam classifier

Week 5: Projects

– Telecom Customer Churn Prediction
– Insurance Claim Fraud Detection
– Gold Price Prediction
– Credit Card Fraud Detection
– Natural Scene Text Detection
– IDB – Income Qualification Prediction

Who can attend?

  • Engineers
  • Software/ IT / Data Professionals
  • Engineering students/Professors
  • Hobbyists

Minimum Eligibility

  • Should have knowledge on at least one of the programming languages (C / C++/ Python / Java / R).
  • Should be ready to commit to rigorous training and learning
  • Only students who finished level 1 can attend level 2

How Classes will happen?

  • Our trainers will take live classes online
  • Weekdays – 2 hours each session. Total of 15 sessions.
  • Weekends – 3 hours each session. Total of 10 sessions.
  • Assignments, Handouts / Quiz will be done through our e-Learning portal

Course Fees

Level 1 – Rs.4250

Fees is inclusive of Online training, access to our e-Learning portal, Recorded session videos for future reference, Certification & GST

Level 2 – Rs.5500 ( Inclusive of GST)

Upcoming Batches

Weekend Batch: Sep 05th – Oct 04th, 2020

Timing: 9 am to 12 pm

Weekdays Batch: Sep 14th – Oct 16th, 2020

Timings: 5 pm to 7 pm

Fee Inclusions
  • 6 weeks of online training
  • Access to our e-Learning portal
  • Project Guidance & Mentorship
  • Certification
  • 18% GST
Infrastructure Requirements
  • Good Internet Facility
  • Windows or Mac with decent specification

Only 25 slots per batch. Get yours before it gets filled!

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