What you'll get
- 125+ Hours
- 39 Courses
- Mock Tests
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
- Download Curriculum
Synopsis
- Comprehensive training in Python-powered Data Science, Machine Learning, and Artificial Intelligence.
- Covers Python programming, data analysis with Pandas and NumPy, data visualization, OpenCV video analytics, statistics, and machine learning algorithms.
- Hands-on projects provide practical experience across real-world scenarios.
- One-year unlimited access to all learning materials.
- Ideal for learners aiming for careers in data analytics with basic Python knowledge.
- Completion certificates are provided for each module.
- Verifiable certificates with unique links suitable for resumes and LinkedIn.
- Self-paced video format allows flexible learning.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning with Python Course | 5h 17m | ✔ | View Curriculum |
| Project on Machine Learning - Covid19 Mask Detector | 2h 05m | ✔ | View Curriculum |
| Machine Learning Project - Auto Image Captioning for Social Media | 2h 31m | ✔ | View Curriculum |
| Machine Learning with Scikit Learn | 8h 37m | ✔ | View Curriculum |
| Predictive Modeling with Python | 8h 26m | ✔ | View Curriculum |
| Machine Learning using Python | 3h 26m | ✔ | View Curriculum |
| Data Science with Python Training 2022 | 11h 18m | ✔ | View Curriculum |
| Matplotlib Basic | 4h 2m | ✔ | View Curriculum |
| Matplotlib Intermediate | 2h 53m | ✔ | View Curriculum |
| Matplotlib Advance | 6h 37m | ✔ | View Curriculum |
| Pandas with Python Tutorial | 5h 42m | ✔ | View Curriculum |
| Numpy and Pandas | 5h 9m | ✔ | View Curriculum |
| Pandas Project | 3h 14m | ✔ | View Curriculum |
| Sentiment Analysis with Python | 57m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Seaborn | 2h 28m | ✔ | View Curriculum |
| Seaborn Intermediate | 1h 18m | ✔ | View Curriculum |
| Seaborn Advance | 1h 56m | ✔ | View Curriculum |
| Pyspark Beginner | 2h 16m | ✔ | View Curriculum |
| Pyspark Intermediate | 2h 02m | ✔ | View Curriculum |
| Pyspark Advance | 1h 18m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Data Science with Python | 4h 14m | ✔ | View Curriculum |
| Artificial Intelligence with Python - Beginner Level | 2h 51m | ✔ | View Curriculum |
| Artificial Intelligence with Python - Intermediate Level | 4h 34m | ✔ | View Curriculum |
| Artificial Intelligence with Python | 6h 15m | ✔ | View Curriculum |
| OpenCV for Beginners | 2h 28m | ✔ | View Curriculum |
| Video Analytics Using Opencv and Python Shells | 2h 13m | ✔ | View Curriculum |
| Statistics for Data Science using Python | 3h 23m | ✔ | View Curriculum |
| Tensorflow With Python | 1h 46m | ✔ | View Curriculum |
| Applied Data Analytics Using Python | 5h 7m | ✔ | View Curriculum |
| Random Forest Algorithm in Machine Learning | 1h 27m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Python for Finance | 1h 7m | ✔ | View Curriculum |
| Financial Analytics with Python | 1h 6m | ✔ | View Curriculum |
| Project on Linear Regression in Python | 2h 28m | ✔ | View Curriculum |
| House Price Prediction using Linear Regression | 3h 2m | ✔ | View Curriculum |
| Logistic Regression-Predicting the Survival of Passenger in Titanic | 2h 6m | ✔ | View Curriculum |
| Credit-Default using Logistic Regression | 3h 3m | ✔ | View Curriculum |
| Forecasting the Sales of the Store Using Time Series Analysis | 2h 13m | ✔ | View Curriculum |
| Data Science with Python Project-Predict Diabetes on Diagnostic Measures | 1h 02m | ✔ | View Curriculum |
| Develop Movie Recommendation Engine using Machine Learning | 51m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| No courses found in this category. | |||
Description
This program offers a structured, end-to-end pathway to mastering machine learning and AI using Python. Designed for both aspirants and working professionals, the course combines foundational theory with extensive hands-on exercises. Participants gain practical skills in Python-driven analytics, enabling them to confidently apply machine learning, statistical modeling, and AI techniques to real-world problems.
The course begins with core machine learning principles, introducing supervised and unsupervised learning, data preprocessing, model evaluation, and deep learning concepts, including image captioning. Each module balances theory with practical exercises to strengthen programming and analytical skills.
Next, the program deepens Python expertise through data visualization and manipulation. Learners explore Matplotlib and Seaborn for insightful visualizations, and Pandas and NumPy for efficient data processing, enabling robust analysis and informed decision-making.
Advanced modules focus on AI applications, including computer vision with OpenCV, deep learning with TensorFlow, and advanced statistical techniques. Participants develop the capability to tackle complex AI challenges and implement innovative solutions.
The course culminates with practical projects covering predictive modeling, regression, and recommendation systems. These exercises solidify understanding, develop problem-solving abilities, and offer real-world experience.
Skill assessment through quizzes and mock tests ensures learners are ready for certification and professional application of their knowledge.
Sample Certificate
Goals
- Equip learners with Python-based machine learning and AI skills.
- Enable practical application of data science concepts through hands-on projects.
- Build confidence in using Python for predictive analytics, computer vision, and statistical analysis.
- Prepare learners for career opportunities in data-driven industries.
Objectives
- Understand fundamental and advanced machine learning concepts.
- Gain proficiency in Python for data processing, analysis, and visualization.
- Apply AI techniques, such as computer vision and deep learning, to practical scenarios.
- Learn to analyze data and convey meaningful insights with clarity.
- Build a portfolio of projects demonstrating practical expertise.
Highlights
- Structured learning from basics to advanced machine learning and AI.
- Hands-on projects and real-world case studies.
- Master Python libraries: Pandas, NumPy, Matplotlib, Seaborn, OpenCV, TensorFlow.
- Flexible, self-paced video lessons are available for 1 year.
- Mock tests and quizzes to evaluate knowledge and skills.
- Completion certificates with verifiable links for professional use.
Requirements
- Basic Python knowledge, including syntax, data structures, functions, and modules.
- Understanding of fundamental mathematics: algebra, calculus, probability, and linear algebra.
- Understanding fundamental statistical measures, including average, median, mode, variability, and standard deviation.
- Optional: prior exposure to data manipulation and visualization libraries like Pandas and Matplotlib.
- Curious and persistent in exploring evolving machine learning and AI technologies.
Target Audience
- Aspiring data scientists seeking Python and AI expertise.
- Software developers are expanding into data analysis and machine learning.
- Students and researchers are pursuing advanced knowledge in data science.
- Professionals applying data-driven insights to business or research decisions.
- Career changers aiming to build foundational Python and ML skills.
- Entrepreneurs leveraging AI and analytics for business growth.
FAQ
Q1. Do I need advanced Python skills to enroll?
No, basic Python knowledge is sufficient. The course introduces advanced concepts progressively.
Q2. How long can I access the course content?
Learners get one-year access to all materials.
Q3. Are projects included?
Yes, each section includes hands-on projects to apply learned skills.
Q4. Will I receive a certificate?
Yes, verifiable certificates are provided for each completed module, ideal for resumes and LinkedIn.
Q5. Is the course self-paced?
Yes, learners can progress at their own pace.
Career Benefits
- Strong foundation in Python-based machine learning and AI.
- Practical experience through hands-on projects to enhance employability.
- Ability to implement data-driven solutions across industries.
- Portfolio-ready work demonstrating applied analytics skills.
- Competitive edge for roles in data science, AI, and analytics-driven careers.
- Enhanced credentials for professional advancement and networking opportunities.
