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View Batch Details

Batch 01 - [ 20-May-2024 09-Sep-2024 ]

Core Java & Concepts Course - Crash Course

Staff : Vasu A

Batch 01

Core Java & Concepts Course
Crash Course - V 2.5

Class A

20-Feb-2024

20-May-2024 | 09-Sep-2024

Weekday Slot

Morning Slot

1st Floor, 2nd Class

20 / 45 42 %

11:00 AM 12:30 AM

Both

Projector, Wifi

Mon, tue, Wed, Thu, Fri

Scheduled

- Not Scheduled

Cancelled

Staffs	03/06 Mon	04/06 Tue	05/06 Wed	06/06 Thu	07/06 Fri	10/06 Mon	11/06 Tue	12/06 Wed	13/06 Thu	14/06 Fri	17/06 Mon	18/06 Tue	19/06 Wed	20/06 Thu	21/06 Fri	24/06 Mon	25/06 Tue	26/06 Wed	27/06 Thu	28/06 Fri	Total No Of Days
Vasu A Trainer Primary		-													-						17 / 45 38 %
Harini S Trainer Alternate	-		-	-	-	-	-	-	-		-	-	-	-		-	-	-	-	-	02 04 %

P Present

A Absent

L Leave

Excellent

Good

Average

Poor

Students	03/06 Mon	04/06 Tue	05/06 Wed	06/06 Thu	07/06 Fri	10/06 Mon	11/06 Tue	12/06 Wed	13/06 Thu	14/06 Fri	17/06 Mon	18/06 Tue	19/06 Wed	20/06 Thu	21/06 Fri	24/06 Mon	25/06 Tue	26/06 Wed	27/06 Thu	28/06 Fri	Total Days
Priya F LD-0003/24	P	A	P	L	P	A	P	A	P	L	A	P	P	P	P	P	P	P	P	P	14 / 45 31 %
Nandhini F LD-0002/24	P	P	P	P	A	P	P	P	P	P	P	A	P	P	P	L	P	P	P	P	17 / 45 38 %
Praveen M LD-0001/24	P	P	P	P	P	P	P	P	P	P	P	P	P	P	P	P	P	P	P	P	20 / 45 44 %

01. Getting Started

01. Introduction to Python

100% (30-May-2024)

a. History and Features of Python

b. Basic Syntax

c. Variables and Data Types

d. Operators

e. Conditional, Loop & Control Statements

f. Functions

g. Random Modules

02. Introduction to Data Science

100% (30-May-2024)

a. Data Science

b. Data Science vs. Data Scientist

c. Future scope of data scientist

d. How to link python with DS

03. Basic Terminologies of DS

100% (30-May-2024)

a. Data Mining

b. Data Sets

c. Python packages

d. Data Collections

e. Data Preprocessing

f. Data Visualization

g. Data Modelling

04. Installation of Packages

100% (30-May-2024)

a. Pandas

b. Numpy

c. Scipy

d. Sckit

e. NLTK

05. Python Scientific Libraries

100% (30-May-2024)

a. Pandas

b. Numpy

c. Scipy

d. Sckit-Learn

e. NLTK

f. Matplot

g. Keras

h. Tensorflow

i. PyTorch

02. Data Collection and Preprocess

01. Introduction to Numpy

100% (30-May-2024)

a. Numpy

b. Create Numpy arrays

c. Numpy operations

d. Numpy for statistical operations

02. Data Preprocessing

100% (30-May-2024)

a. Handling Missing/Fill-na/Replace Values

b. Drop Column/row

c. Label Encoding

d. One-Hot Encoding

e. Reshaping

f. Data operations

g. Data frame creations

h. Statistical functions in data operations

i. Merging and joining data frame

j. Concatenate

03. Data Normalization

100% (30-May-2024)

a. What is Data Normalization?

b. Standard Scalar

c. Min-Max Scalar

d. Hands on standard scalar

e. Hands on min-max scalar

04. Data Collection

100% (30-May-2024)

a. How to collect input data

b. Read CSV Data

c. Read JSON Data

d. Read XLS Data

e. Read HTML Contents

f. View Data

03. Visualizations

01. Matplotlib Visualization

100% (30-May-2024)

a. Plotting with matplotlib

b. Bar charts

c. Pie charts

d. Scatter plots

e. Box plots

f. Histogram

g. Bubble Chart

h. Heat maps

i. Graph/line graph

j. Geographical data

02. Seaborn Visualization

100% (30-May-2024)

a. Plotting with seaborn

b. Histogram with grid

c. Distplot

d. Pairplot

e. Scatter plots

f. Box plots

g. Lmplots

h. Histogram

i. Challenge: Visualize titanic dataset

03. Feature Engineering

100% (30-May-2024)

a. Feature Extraction

b. Feature Selection

c. EDA

d. Pairplot

e. Scatter plots

f. Box plots

g. Lmplots

h. Histogram

i. Challenge: Visualize titanic dataset

04. File Handling

100% (30-May-2024)

a. open() mode

b. read() mode

c. write() mode

d. append() mode

04. Machine Learning

01. Machine Learning

100% (30-May-2024)

a. Al vs ML vs DL

b. Application of machine learning

c. How do machine learns

d. Types of Machine learning

e. Supervised Learning

f. Un-supervised Learning

02. Naive Bayes

100% (30-May-2024)

a. What is Native Bayes?

b. Types of Naive Bayes

c. IRIS Classification

d. Data Processing

e. Train and create model

f. Performance Estimation

g. Analyse and create confusion matrix

03. Simple Linear Regression

100% (30-May-2024)

a. Ordinary Least Square and Regression Errors

b. Data Processing

c. Train and Test Model

d. Test the model and Predict Y Values

e. R-Squared and its Importance

f. Score and Get coefficients

g. Calculate RMSE (Root Mean Squared Error)

h. Plot the predictions

04. Support Vector Machine

100% (30-May-2024)

a. What is SVM?

b. SVM kernel types

c. Problem: IRIS Classification

d. Data Processing

e. Train and create model

f. Performance Estimation

g. Analyse and create confusion matrix

05. Algorithms

01. Logistic Regression

100% (30-May-2024)

a. What is Logistic Regression?

b. Problem: Heart Disease Prediction

c. Build Model

d. Performance Estimation

e. Analyse and create confusion matrix

02. Decision Tree

100% (30-May-2024)

a. What is decision tree?

b. Decision Tree Parameters

c. Problem: IRIS Classification

d. Data Processing

e. Train and create model

f. Evaluate Model

03. Random Forest

100% (30-May-2024)

a. What is random forest?

b. Ensemble Learning

c. Bagging and Boosting Classifiers

d. Problem: Cardio Vascular Disease

e. lmplementation

f. Evaluate Model

04. Nearest Neighbor

100% (30-May-2024)

a. What is KNN?

b. KNN parameters

c. Problem: Cardio Vascular Disease

d. Data collection and preprocessing

e. lmplementation

f. Evaluate Model

06. Evaluation Metrics

01. Evaluate Classification metrics

100% (30-May-2024)

a. Evaluate Accuracy

b. Classification metrics

c. What is Threshold and PHAPTER Adjusting Thresholds

d. AUC ROC Curve

02. Dimension Reduction

100% (30-May-2024)

a. Why to reduce dimensions and Importance of PCA?

b. Steps to calculate PCA

c. Implementation of PCA

d. Visualization

03. Regression

100% (30-May-2024)

a. What is Ridge regression?

b. Implement Ridge Regression

c. Plot Ridge Regression Line

d. Lasso Regression or L1 Penalty

e. lmplement lasso Regression

f. Plot lasso Regression Line

04. Over fitting and under fitting

100% (30-May-2024)

a. What is over fitting?

b. How to avoid over fitting?

c. What is under fitting?

d. How to avoid under fitting?

05. Cross Validation

100% (30-May-2024)

a. What is Cross Validation?

b. How Cross Validation Works

c. Prepare for Cross Validation

d. Parameter and implementation of Cross Validation

e. Understand the results of Cross Validation

f. Hands On - Analyse the Result

07. Hypertunning Model and Clustering

01. Hyper Tuning for model

100% (30-May-2024)

a. What is Hyper parameter Tuning?

b. Grid Search and Randomized Search Approach

c. Grid SearchCV Parameters Explained

d. Create Gird SearchCV Object

e. Fit data to GridSearchCV

f. Understand GridSearchCV Results

02. Implementation of hypertuning

100% (30-May-2024)

a. Grid SearchCV using Logistic Regression

b. Grid SearchCV using Support Vector

c. Randomized Search using random forest

d. Select Best Model

e. Randomized Search

f. Model Selection Summary

03. Clustering

100% (30-May-2024)

a. What is Clustering?

b. How the clusters are formed?

c. Problem Understanding: Customer Segmentation

d. Get, Visualize and Normalize the data

e. Import KMeans and Understand Parameters

f. Understanding KMeans++ Initialization Method

g. Create Clusters

h. Visualize and create different number of clusters

i. Understand Elbow Method to Decide number of Cluster

j. Implement Elbow Method

08. NLP and Recommendation System

01. NLP

100% (30-May-2024)

a. What is NLP

b. Application of NLP

c. Remove punctuation

d. Tokenize

e. Remove stop words

f. Stem words

g. Lemmatize

h. Padding

i. Part of Speech Tagging

j. Name Entity Relationship

k. Sentiment Analysis

02. Build our spam detector

100% (30-May-2024)

a. Read and preprocess data

b. NLP techniques

c. Tokenization

d. Tokens to vectors

e. Naive Bayes

f. Logistic Regression

g. Sentiment Analysis

h. Evaluate Metrics

03. Recommendation System

100% (30-May-2024)

a. What is Recommendation System?

b. How Do Recommendation Works?

c. Types of Recommendation

04. Content Based Recommendation

100% (30-May-2024)

a. What is Content based recommendation?

b. Advantages and drawbacks

c. Hands on Content based recommendation code

05. Collaborative Filtering

100% (30-May-2024)

a. What is Collaborative filtering?

b. Advantages and drawbacks

c. Kinds of collaborative filtering

d. Hands on collaborative filtering code

06. Hybrid Recommendation System

100% (30-May-2024)

a. What is hybrid recommendation?

b. Advantages and Drawbacks

09. Deep Learning

01. Deep Learning

100% (30-May-2024)

a. What is Neuron and Artificial Neural Network?

b. How Artificial Neural Network works?

c. What is Keras and Tensorflow?

d. What is a Tensor in Tensorflow?

e. Installing Keras, backend and Tensorflow

02. Optimization

100% (30-May-2024)

a. Keras Model Building and Steps

b. Layers - Overview and Parameters

c. Activation Functions

d. Layers - Softmax Activation Function

e. What is a Loss Function?

f. Cross Entropy Loss Functions

g. Optimization - What is it?

h. Optimization - Gradient Descent

i. Optimization - Stochastic Gradient Descent

j. Optimization - SGD with Momentum

k. Optimization - SGD with Exponential Moving Average

l. Optimization - Adagrad and RMSProp for learning rate decay m. Optimization - Adam

03. Deep Neural Layers

100% (30-May-2024)

a. Initializers - Vanishing and Exploding Gradient Problem

b. Layers - Initializers explained

c. Problem Understanding: Disease Prediction

d. Read and process the data

e. Define the Keras Neural Network Model

f. Compile the Keras Neural Network Model

g. Evaluate the result

10. Projects

01. Project on Application of data science Part 1

100% (30-May-2024)

a. Problem understanding: Loan Approval Prediction

b. Read and preprocess data

c. Data splitting

d. Classification - Naïve Bayes

e. Classification – Support Vector Machine

f. Classification - Random Forest

g. Classification - Logistic Regression

h. Train the model

i. Evaluate performance metrics

j. Visualization using matplotlib and seaborn

02. Project on Application of data science Part 2

100% (30-May-2024)

a. Problem Understanding : Zomato Restaurant Review

b. Read and preprocess data

c. NLP

d. Data Splitting

e. Classification - Naïve Bayes (Multinomial)

f. Classification - Support Vector Machine

g. Classification - Random Forest

h. Classification – Decision Tree

i. Classification - Logistic Regression

j. Train the model

k. Evaluate performance metrics

l. Visualization using matplotlib and seaborn

03. Project on Application of data science Part 3

100% (30-May-2024)

a. Problem Understanding : Product Recommendation

b. Read and preprocess data

c. Collaborative filtering

d. Content based filtering

e. Recommend the product

f. Evaluate metrics

View Batch Details

Batch 01 - [ 20-May-2024 09-Sep-2024 ]

Core Java & Concepts Course Crash Course - V 2.5

Core Java & Concepts Course
Crash Course - V 2.5