Dlithe Internship Technical Blog

Internship Details-

April,20 2021

Hi.. my name is Vedanth M. I'm  a ISE Engineering Student of Alvas Institute of Engineering and Technology. I'm currently doing my internship at Dlithe Consultancy Services Pvt.Ltd. Here is the brief summary of the things learnt in the 15 days training session are as follows:

DAY 1 - 08/03/2021:

• Brief session on basic concepts in python.
• Practiced some of the basic codes.
• Emoji installation.

DAY 2 - 09/03/2021:

• Decision making.
• Tricks.
• Profiling.
• Getting started with Numpy Library.
• Learnt about Lambda function.
• Howdoi python module.

DAY 3 - 11/03/2021:

• Pandas Library.
• Array operations.
• Codes in Python using Pandas library.
• Worked with USAhousing text file.

DAY 4 - 12/03/2021:

• How to Apply Linear Regression.
• Obtaining correlation.
• Performing Gradient Descent.

DAY 5 - 13/03/2021:

• Logistic Regression.
• Worked on the dataset titanic_train.csv .
• Generated the Confusion Matrix and obtained the accuracy score.
• Exploratory Data Analysis.

DAY 6 - 15/03/2021:

• K-Nearest Neighbors Algorithm.
• Importing libraries and dataset.
• Visualizing the Training and Testing set results.
• Feature Scaling.
• Training Naive Bayes Model.

DAY 7 - 17/03/2021:

• Learnt about Naive Bayes Theorem.
• advanced fundamental model of Bayes Theorem.
• Splitting dataset into train and test.
• Feature Scaling.
• Training Random Forest Classification Model.

DAY 8 - 19/03/2021:

• Decision Trees and Random Forest .
• Importing libraries and dataset.
• Learnt about K-means clustering.
• Training the k-means model.
• Using the elbow method.

DAY 9 - 20/03/2021:

• Visualizing the Clusters.
• Using the Dendrogram.
• Worked on the dataset Mall_Customers.csv.
• Training the hierarchical clustering model.

DAY 10 - 22/03/2021:

• Hierarchical Clustering . 
• Worked on the dataset Mall_Customers.csv .
• Using the Dendogram to find optimal number of clusters.
• Training the Apriori model.
• Data preprocessing.

DAY 11 - 23/03/2021:

• Learnt about Apriori Algorithm.
• Displaying the first results coming directly from the output of the apriori function.
• Cleaning the data.
• Creating bag of words.
• Displaying the results non sorted.
• Splitting the dataset.
• Training the Naive Bayes Model.

DAY 12 - 24/03/2021:

• Natural Language Processing.
• Splitting the dataset into Training and Testing set.
• R squared in regression.
• Accuracy paradox.
• CAP analysis.

DAY 13 - 25/03/2021:

• Learnt in brief about R squared.
• Applying the k-fold cross validation.
• Training the kernel SVM model.
• Splitting the dataset into components.
• Training the XGBoost.
• Visualizing the results.

DAY 14 - 27/03/2021:

• Applying K-fold Cross Validation and predicting the value.
• Training XGBoost on the Training Set.
• Discussed real world examples using Machine learning.
• Summarized the things which were taught in previous classes.

DAY 15 - 28/03/2021:

• Use Cases on Linear Regression and other algorithms.
• Worked on chatbot application.