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.
Watch Video
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!
Feedback from our ML Course Classroom program
How well participants were able to understand the concepts explained by the trainer
Rating for the training session from the participants
