Data Science With Deep Learning Online Training
What is Data Science With Deep Learning : “Data Science With Deep Learning” is the combination of data analysis, machine learning, and advanced neural networks to extract insights and make intelligent predictions from large and complex datasets. It uses deep learning models like CNNs and RNNs for tasks such as image recognition, natural language processing, and predictive analytics.
Data Science With Deep Learning Online Training
Course Overview Of Data Science With Deep Learning Online Training :
Unlock the full potential of artificial intelligence with
our Data Science with Deep Learning Online Training. This course is
designed for aspiring data scientists and AI professionals who want to master
the foundations of data science and dive deep into cutting-edge deep learning
techniques using Python, TensorFlow, and Keras.
What You Will Learn:
- Fundamentals
of Python and Data Science workflows
- Data
preprocessing and feature engineering
- Neural
networks, CNNs, RNNs, and LSTMs
- Deep
Learning model training using TensorFlow & Keras
- AI
model evaluation, tuning, and deployment
- Real-world
datasets and live project implementations
Enroll Now
And Become Data Science with Deep Learning Expert
Prerequteis Of Data Science With Deep Learning :
1. Basic Programming Knowledge
A solid understanding of programming fundamentals, especially in Python, is essential to work with data and build models.
2. Familiarity with Mathematics & Statistics
Basic knowledge of linear algebra, probability, and statistics will help in understanding machine learning and deep learning algorithms.
3. Knowledge of Machine Learning Concepts (Preferred, Not Mandatory)
While not required, having prior exposure to concepts like regression, classification, or clustering will be an added advantage.
4. Interest in AI and Deep Learning
A strong curiosity and willingness to explore how intelligent systems work is key to succeeding in this field.
5. No Prior Deep Learning Experience Required
This course is designed for beginners and follows a structured approach from foundational to advanced deep learning topics.
Why Choose Us For Data Science With Deep Learning ?
1. Industry-Expert Trainers
Learn from certified data scientists and AI professionals with real-world experience in deep learning and artificial intelligence projects.
2. Comprehensive Curriculum
Master everything from Python programming, data analysis, and machine learning to advanced topics like neural networks, CNNs, RNNs, NLP, and AI deployment.
3. Hands-on Projects with Real Datasets
Apply your knowledge through practical projects using industry-relevant datasets—such as image classification, healthcare diagnostics, and sentiment analysis.
4. Flexible Online Learning
Choose between live instructor-led sessions and self-paced learning options with access to high-quality video tutorials and downloadable resources.
5. Job Assistance & Certification
Benefit from our dedicated placement support, including resume building, mock interviews, and a recognized course completion certificate.
6. Lifetime Access to Course Materials
Get unlimited access to all updated course content, recorded sessions, and project files—even after the course ends.
Course Content Of Data Science With Deep Learning :
MODULE 1: PYTHON FOR DATA ANALYSIS & DATA SCIENCE
1. Introduction to Python for Data Analysis (1 hour)
- Overview
of Python and its applications in data science
- Basics
of Python programming: variables, data types, and basic operations
2. Working with Libraries (1 hour)
- Introduction
to essential libraries: NumPy, Pandas, and Matplotlib
- Basic
operations using NumPy arrays
- Data
manipulation with Pandas DataFrames
- Basic
data visualization with Matplotlib
3. Data Cleaning and Preprocessing (1 hour)
- Handling
missing data
- Removing
duplicates
- Data
normalization and scaling
4. Exploratory Data Analysis (1 hour)
- Descriptive
statistics
- Data
visualization for analysis
- Correlation
and covariance
MODULE 2: ADVANCED DATA ANALYSIS WITH PRACTICAL DATASETS
(5 HOURS)
1. Recap of Python for Data Analysis (1 hour)
- Brief
review of Python basics and key libraries (NumPy, Pandas, Matplotlib)
2. Importing and Exploring Datasets (1 hour)
- Reading
data from various sources (CSV, Excel, SQL)
- Exploring
dataset structure, dimensions, and basic statistics
3. Data Cleaning and Pre-processing (1 hour)
- Handling
missing values
- Dealing
with outliers
- Data
transformation and feature engineering
4. Advanced Data Visualization (1 hour)
- Utilizing
Seaborn for advanced visualization
- Creating
interactive visualizations with Plotly
5. Statistical Analysis and Hypothesis Testing (1 hour)
- Introduction
to statistical concepts
- Performing
hypothesis tests using Python (e.g., t-tests)
6. Practical Data Analysis Project (1 hour)
- Guided
analysis of a real-world dataset
- Applying
learned concepts to solve a specific problem
DATASETS FOR DATA ANALYSIS
Iris Dataset
- Description:
Measurements of sepal length, sepal width, petal length, and petal width
for three species of iris flowers
- Domain:
Botany
- Use:
Classification, basic statistical analysis, visualization
Titanic Dataset
- Description:
Passenger information on the Titanic, including survival status, class,
gender, and age
- Domain:
Transportation
- Use:
Survival analysis, categorical analysis, visualization
Boston Housing Dataset
- Description:
Housing prices and various factors affecting them in Boston suburbs
- Domain:
Real Estate
- Use:
Regression analysis, correlation analysis, visualization
Wine Dataset
- Description:
Chemical analysis results of wines from three different cultivars
- Domain:
Food and Beverage
- Use:
Classification, cluster analysis, visualization
Diabetes Dataset
- Description:
Various health metrics for diabetes patients
- Domain:
Healthcare
- Use:
Regression analysis, correlation analysis, visualization
Heart Disease UCI Dataset
- Description:
Patient data related to heart disease
- Domain:
Healthcare
- Use:
Classification, statistical analysis, visualization
Breast Cancer Wisconsin (Diagnostic) Dataset
- Description:
Biopsy results for breast cancer diagnosis
- Domain:
Healthcare
- Use:
Classification, statistical analysis, visualization
Penguins Dataset
- Description:
Measurements and characteristics of penguin species
- Domain:
Biology
- Use:
Classification, cluster analysis, visualization
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