Training & Duration
- Live classes (Monday to Friday)
- 4 Weeks of Training & 2 Weeks 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.
Certification
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
