Artificial Intelligence + Machine Learning (M)

Artificial Intelligence + Machine Learning (M)


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Slides in English

Explanation in Hindi + English

SKU: cid_128882 Category:
About the course

यह कोर्स हर उस व्यक्ति के लिए design किया गया है जो Artificial Intelligence और Machine Learning और उसके applications के रहस्य को सुलझाना चाहता है। यह हमारे दैनिक जीवन में AI और MLके व्यावहारिक प्रयोग के बारे में है।

इस कोर्स में आपको अलग-अलग libraries के बारे में पता चलेगा, जैसे कि Numpy, Pandas और Scikit Learn। आप कुछ trending concepts जैसे One-hot-encoding, Linear Regression, Polynomial Regression, Overfitting और underfitting, KNN theory, Logistic Regression theory, SVM theory, Theory Naive Bayes Algorithm, Sentiment analysis, Confusion matrix, Hierarchical Clustering के बारे में भी जानेंगे। जिसे आप अपने प्रतिदिन के काम में real-world problems को सुलझाने के लिए software engineer, data scientist और machine learning engineer के रूप में उपयोग कर सकते हैं।

Learning Outcomes

इस कोर्स को पूरा करने के बाद, आप -

  • AI और ML के underlying concepts को समझ पाएंगे
  • आप अपने Datasets के लिए उपयुक्त मॉडल का उपयोग कर सकेंगे
  • हमारे दैनिक जीवन में AI के उपयोग को समझ सकेंगे
  • अपने model को train करके output predict कर पाएंगे
  • Innovative और independent learning के माध्यम से अपनी hireability को बढ़ा पाएंगे
  • कोर्स के सफलतापूर्वक पूरा होने पर certificate प्राप्त कर सकेंगे
Target Audience

The course can be taken by:

Students: All students who are pursuing professional graduate/post-graduate courses related to computer science or Information Technology.

Teachers/Faculties: All computer science and engineering teachers/faculties.

Professionals: All IT professionals in the application development domain.

Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.

2. At the end of the program, a final assessment will be conducted.


1. All successful participants will be provided with a certificate of completion.

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

Topics to be covered

Day's (1, 2)

Chapter 01 - Introduction to Machine learning

  • What is ML?
  • Examples on ML
  • Types of ML
  • Introduction to the basic terminology
  • ML package : scikit-learn
  • Anaconda
  • How to install anaconda

Day's (3, 4, 5, 6, 7)

Chapter 02 - Basic Introduction numpy and Pandas

  • Introduction to NumPy
  • Creating an array
  • Class and Attributes of ndarray
  • Basic Operations
  • Activity-Slice
  • Stack operations
  • Mathematical Functions of NumPy
  • Introduction to Pandas
  • Understanding DataFrame
  • Series
  • Concatenating and appending DataFrames
  • loc and iloc
  • Drop columns or rows
  • Groupby
  • Map and apply

Day's (8, 9)

Chapter 03 - Data Preprocessing

  • Introduction
  • Dealing with missing data
  • Handling categorical data
    • Encoding class labels
    • One-hot-encoding
  • Split data into training and testing sets
  • Bringing Features onto same scale

Day's (10, 11, 12, 13)

Chapter 04 - Regression

  • Introduction
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Evaluate Performance of a linear regression model
  • Overfitting and underfitting
  • Regularization
  • Predicting prices for housing boston dataset

Day's (14, 15)

Chapter 05 - K-Nearest Neighbors(KNN)

  • KNN theory
  • Implementing KNN with scikit-learn
  • KNN Parameters
    • n_neighbors
    • metric
  • How to find Nearest Neighbors
  • Writing Own KNN classifier from scratch

Day's (16, 17, 18, 19)

Chapter 06 – Logistic Regression

  • Logistic Regression theory
  • Implementing Logistic regression with scikit-learn
  • Logistic Regression Parameters
  • Multi-class classification
  • MNIST digit dataset with Logistic Regression
  • Predictive modeling on adult income dataset

Day's (20, 21, 22)

Chapter 07 - Support Vector Machine(SVM)

  • SVM theory
  • Implementing SVM with scikit-learn
  • SVM Parameters:
    • C and gamma
  • Plot hyperplane for linear classification
  • Decision function

Day's (23, 24, 25)

Chapter 08 - Decision Tree and Random Forest

  • Theory behind decision tree
  • Implementing decision tree with scikit-learn
  • Decision tree parameters
  • Combining multiple decision trees via Random forest
  • How random forest works..?

Day's (26, 27, 28, 29)

Chapter 09 - Text Mining and Naïve Bayes Classification

  • Theory Naive Bayes Algorithm
  • Features extraction
    • Countvectorizer
    • TF-IDF
  • Email Spam filtering
  • Sentiment analysis

Day's (30, 31, 32)

Chapter 10 - Model Evaluation and Parameter Tuning

  • Cross validation via K-Fold
  • Tuning hyperparameters via grid search
  • Confusion matrix
  • Recall and Precision
  • ROC and AUC

Day's (33, 34, 35, 36)

Chapter 11 - Clustering and Dimension Reduction

  • K-means Clustering
  • Hierarchical Clustering
  • Elbow method
  • Principal components analysis(PCA)
  • PCA step by step
  • Implementing PCA with scikit-learn
  • LDA with scikit-learn

Day's (37, 38, 39)


  • Email-Spam filtering integrated with Django
  • Sentiment analysis on web app
  • News classification

No experience is required, But fundamental knowledge of C/C++ would be helpful.

  1. Upto six weeks (or till submission of the final quiz) access to the course
  2. To get access to the certificate - you need to take the online MCQ exam (minimum 60%) at the end of the course