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Machine Learning | Weekends - January 2021

This 13 weeks course on ML will help you learn from basics of Machine Learning, understand the math behind algorithms and get hands-on experience working on ML algorithms.


  • Designed for Engineering students
  • 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 that you will 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

Course Curriculum

Click on the headings below to view the detailed curriculum.

- 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

– 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

– 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

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

Problem: Online shoppers' buying intention.

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

Problem: Spam Classifier

– What is Decision Tree?
– Calculating Entropy
– Calculating Information gain
– Cost Complexity Pruning
– Optimising the tree

Problem: Breast cancer detection.

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

Problem: Online shoppers' buying intention, vehicle detection, face detection & object detection.

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

Problem: Movie Recommendation

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

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

Who is this program for?

  • Engineers
  • Software/IT/Data Professionals
  • Engineering students/Professors
  • Predictive Analytics and statistics


  • Engineers
  • Software/IT/Data Professionals
  • Engineering students/Professors
  • Predictive Analytics and statistics

Course Staff

Course Staff Image #1

Benishia B Christo

Senior Trainer, Lema Labs

Course Staff Image #2


Team Lead, Lema Labs

Frequently Asked Questions

I am a beginner, will I be able to understand the course?

Yes! The course is designed keeping you (Beginners) in mind. Frequent quizzes, Interactive videos and other activites are planned to ensure you are able to easily learn all the fundamentals

I already know a few basics, is the course right fit for me?

Yes! The course is designed keeping you too in mind! You can skip through the portions that you are already comfortable with and directly attempt Knowdge checks to unlock the next lessons.