Data Science Course – Learn Python, Machine Learning & AI | 100% Job Assistance
Best Data Science Course in Palakkad – Learn Python, AI & Machine Learning
Are you looking for
the best data science course in Palakkad?
Unlock high-paying career opportunities by mastering Data Science, Machine Learning, and AI from top industry
experts. Our advanced training program is designed for students, graduates, and working professionals in
Palakkad who want to build a successful career in the booming data industry.
This comprehensive
course includes hands-on training in Python
programming, data analysis, SQL, statistics, machine learning algorithms,
and real-world projects. Whether
you're a beginner or an IT professional, our course equips you with the
practical skills and certifications needed to stand out in today’s job market.
Located in the heart of Palakkad, Kerala, our data science institute offers both offline and online training, weekend batches, career mentoring, and 100% placement support. Join hundreds of successful learners who have launched their careers in data science with us!
Master Data Science with our hands-on training in Python, Machine Learning, and AI. Work on real-world projects, get certified, and land high-paying jobs in data science. Online + offline batches available.
· End-to-end curriculum covering Python, SQL, Machine Learning, Deep Learning & Big Data.
·
Real-world projects using datasets from healthcare, finance,
marketing, and e-commerce.
·
Taught by industry-certified
mentors with experience in top MNCs.
·
Includes preparation for certifications like
IBM, Google Data Analytics, and Microsoft Azure.
·
Career support with resume building, interview training, and
placement assistance.
·
What You'll Learn
·
Python for Data Analysis
·
Exploratory Data Analysis (EDA)
·
SQL & Data Manipulation
·
Machine Learning Algorithms
·
Deep Learning & Neural Networks
·
Natural Language Processing (NLP)
·
Data Visualization with Tableau/Power BI
·
Big Data (Hadoop, Spark basics)
· Capstone Projects & Deployment
Career Outcomes
·
Data Analyst
·
Data Scientist
·
Machine Learning Engineer
·
Business Intelligence Analyst
·
AI Engineer
·
Data Engineer
Machine learning Basics
Artificial Intelligence
Machine Learning
Types of machine learning
Supervised Learning
Un supervised learning
Reinforcement Learning
Deep learning
Basics
Neural network
Applications
Google Colaboratory
Joining 2 strings
Data types in Python
List
tuple
Dictionary
Operators
Condition statements
Indentation
Loops
functions
(Numpy, Pandas, Matplotlib, Seaborn)
Numpy
Numpy Arrays
Numpy Arrays Functions
Analysing a numpy array
Mathematical operation in a
Numpy array
Array Manipulations
Pandas
Pandas dataframes
creating Pandas data frames
Importing data from csv to pandas
frames
Loading the data from Excel file
using pandas data frames
EXporting data frame to csv file
To Export the pandas dataframes
to the excel file
Inspecting data frame
Manipulating data frame
Correlation
Matplotlib
Wave Plot
Parabola
Graph with other symbol
Multiline Plot
BAR PLOT
Pie Chart
Scatter Plot
3d Scatter Plot
Seaborn
Default data sets
Setting Default Theme
RelPlot
Difference Between
matplotlib and sea born
ScatterPlot
Count Plot
Bar Chart
Distribution Plot
Correlation Plot- Heat map
Where to collect the data
Handling Missing value indataset
Methods to handle missing values
Missing values
Imputation
Central Tendencies
Filling with Median value
Filling with mean value
Filling the Mode value
Dropping
Data standardization
Sklearn Preprocessing
Splitting Standardizing
Label Encoding
Label encoding for Breastcancer
Label encoding for Iris flower
Train Test split
Imbalanced data set
Under sampling
Feature Extraction
BAG OF WORDS(BOW)
Term frequency-Inverse
Document Frequency (TF-IDF)
Strumming
Numeric Dataset pre-processing-Diabetes-Numeric- use case
Data collection and preprocessing
Separating the features and target
Data standardization
Splitting
Text data dataset Preprocessing- use case
Finding Stop words
Data pre-processing
Checking for missing data values
Replace the missing values
Creating the logical data
Separating the content (feature) and the label (target)
Stemming
Creating a function to stem the given content
Applying the content values in to the above function
Feature Extraction
Use case1:
Use case2:
Use case3:
Linear Algebra
Vectors
Vector Operations 2
Matrix - Basics
Matrix Operations
Matrix Operations in Python
Statistics
Statistical measures
Roles of Statistics in ML
Need of Statistics
Few applications of statistics
Types of Data
Types of statistics
Types of Study
Population and sample
Measures of central Tendencies
Measure of variability
Percentiles and Quantiles
Correlation and Causation
Hypothesis Hypothesis Testing
Probability
Basics of probabilities
Random Variables
Type of data
Probability distribution for Random variable
Normal Distribution and skewness
Poisson distribution
Calculus
Differential calculus
Integration Calculus
Machine Learning Model
Model selection
Cross Validation
Overfitting in Machine learning
Underfitting in Machine learning
Bias- Variance Tradeoff
Loss function in machine learning
Model Evaluation in Machine learning
Accuracy score
Mean squared error
Type of Model parameters
Gradient Descent in Machine Learning
INTRODUCTION TO DJANGO
What is Django?
Features and advantages of Django
MVC vs MVT architecture
Installing Django and setting up a virtual environment
Creating a basic Django project
Django Project Structure
Understanding settings.py, urls.py, views.py, and models.py
Django Admin Interface
Running the development server
URL Routing and Views
Mapping URLs to views
Writing views (function-based views)
HttpResponse and rendering templates
URL parameters and named routes
Templates
Creating and using HTML templates
Django Template Language (DTL)
Template inheritance (base.html)
Static files (CSS, JS, images)
Models and Databases
Introduction to Django ORM
Defining models
Database migrations
CRUD operations using models
Admin customization
Forms and User Input
Django form basics
Handling GET and POST requests
Form validation
CSRF protection
Django Admin
Creating superuser
Customizing the admin interface
Registering models in adminSAFETechnologies98