Artificial Intelligence + Machine Learning (WT)


Artificial Intelligence + Machine Learning (WT)


Course fee including GST.

Please login to purchase the course.

The batch will start the next day of the registration date


You will get:

On successful completion of your training, you will get three things:

  • Certificate of Completion
  • Project Letter
  • Internship Letter


New batch starting from 25th January, 2022


07:00 to 08:00

Course Features

  • Duration: 4 Weeks of Training & 2 Weeks Project Work
  • Delivery Mode: Live Sessions
  • Updated Quality content: Content is the latest and gets updated regularly to meet the current industry demands.

Target Audience

  • Students: All students who are looking forward to acquiring new skills.
  • Teachers/Faculties: All teachers/faculties who wish to acquire new skills or improve their existing skills.
  • Professionals: All working professionals, who wish to acquire new skills or improve their existing skills.


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.

Fundamental knowledge of Python is required.

Topics to be covered

  • Introduction to Machine learning(ML)
  • Overview of major types: supervised and unsupervised
  • Major steps in ML, Overview of NumPy library
  • Pandas, data structure in pandas: Series and DataFrame
  • Introduction to Matplotlib
  • Linear Regression Algorithm, Mean squared error(MSE), R2_score
  • Non-linear Regression, Overfitting, and Underfitting
  • Introduction to KNN (K Nearest Neighbor), working of KNN, Decide the value of K, Accuracy score
  • Evaluation technique for classification, confusion matrix, Recall, Precision
  • Hyper-parameter tuning using GridSearchCV
  • Introduction to Logistic Regression, working of it, Binary classification and Multi-class classification
  • Feature extraction, Bag of words, Countvectorizer and TfidfVectorizer
  • Introduction to Naive Bayes algorithm and working of Naive Bayes algorithm
  • SVM (support vector Machine), linear and Non-linear SVM, decide Hyperparamters C and kernel
  • Hyper-parameter tuning of C and kernel
  • Introduction to Decision tree algorithm, Gini Index, Pruning technique
  • What is a Random Forest algorithm? Working on it, and how does it differ from the decision tree?
  • PCA, working of PCA, steps in PCA
  • What is clustering?, K-means clustering algorithm, Elbow method
  • Basic Overview of Neural Network, Single-layer Neural network, and Multi-layer neural Network
  • Keras API, Activation functions, feed-forward Neural network
  • Basic Introduction to Convolutional Neural Network(CNN), CNN Architecture, Convolution layer, Pooling layer,
  • Dense layer
  • Time-saving & Cost-effective
  • Get trained via industry experts (having 10+ years of experience in the same field, corporate trainers)
  • Full of hands-on practical exposure for better understanding
  • Adding super solid value in your professional career
  • Weekend Doubt clearing sessions.
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