4_Artificial Intelligence + Machine Learning (with Project Letter)

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4_Artificial Intelligence + Machine Learning (with Project Letter)


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Training & Duration

  • Live classes (Monday to Friday)
  • 4 Weeks of Training & 2 Weeks of Project Work

Target Audience

  • B.Tech/MCA/BCA/M.Tech Students
  • Working Professionals from Corporate

Course Features

  • Online lectures: Online live lectures.
  • Updated Quality content: Content is the latest and gets updated regularly to meet the current industry demands.

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.

  • Basic knowledge of computers.
  • Knowledge of Python is essential.

If you are not familiar with Python suggested course is.

Topics to be covered
  • Introduction to Machine learning(ML)
  • Overview of major types: supervised and unsupervised
  • Major steps in ML, Overview of NumPy library
  • Lab: AIML Lab 1
  • Pandas, data structure in pandas: Series and DataFrame
  • Introduction to Matplotlib
  • Lab: AIML Lab 2
  • Linear Regression Algorithm, Mean squared error(MSE), R2_score
  • Lab: AIML Lab 3
  • Non-linear Regression, Overfitting, and Underfitting
  • Lab: AIML Lab 4
  • Introduction to KNN (K Nearest Neighbor), working of KNN, Decide the value of K, Accuracy score
  • Lab: AIML Lab 5
  • Evaluation technique for classification, confusion matrix, Recall, Precision
  • Hyper-parameter tuning using GridSearchCV
  • Lab: AIML Lab 6
  • Introduction to Logistic Regression, working of it, Binary classification and Multi-class classification
  • Lab: AIML Lab 7

Project: AIML Project - 1

  • Feature extraction, Bag of words, Countvectorizer and TfidfVectorizer
  • Lab: AIML Lab 8
  • Introduction to Naive Bayes algorithm and working of Naive Bayes algorithm
  • Lab: AIML Lab 9
  • SVM (support vector Machine), linear and Non-linear SVM, decide Hyperparamters C and kernel
  • Hyper-parameter tuning of C and kernel
  • Lab: AIML Lab 10
  • Introduction to Decision tree algorithm, Gini Index, Pruning technique
  • Lab: AIML Lab 11
  • What is a Random Forest algorithm? Working on it, and how does it differ from the decision tree?
  • Lab: AIML Lab 12
  • PCA, working of PCA, steps in PCA
  • Lab: AIML Lab 13

Project: AIML Project - 2

  • What is clustering?, K-means clustering algorithm, Elbow method
  • Lab: AIML Lab 14
  • Basic Overview of Neural Network, Single-layer Neural network, and Multi-layer neural Network
  • Keras API, Activation functions, feed-forward Neural network
  • Lab: AIML Lab 15
  • Basic Introduction to Convolutional Neural Network(CNN), CNN Architecture, Convolution layer, Pooling layer,
  • Dense layer
  • Lab: AIML Lab 16

Project: AIML Project - 3

Doubt clearing session

For inquiry call:  8953463074

Online Live Training Program 2023


Andaleeb Tarannum

Andaleeb Tarannum

I was very enthusiastic about learning AI- ML And hence decided to take the course from IIT Kanpur. It was an excellent experience

Arsh Mishra

Arsh Mishra

A good start for pursuing a career in the field of Artificial Intelligence

Atul Bhatia

Atul Bhatia

We are in the process of implementing AI ML based business solutions, it will help in solutioning and smooth implementation.

Neha Pandey

Neha Pandey

I am studying master's in data science at the University Of Canberra. Also, I work as a research analyst for the Institute Of Economic Growth. Before joining this course I was not aware of the fact that it is so interesting and doable. Now I have an idea and familiar with the terminologies and ready to learn more. My understanding is that in the next 4-5 years, India will be the center of attraction for a data scientist career.