Python for Data Science: Mastering Machine Learning and AI
Specialization | 39 Course Series | 6 Mock Tests
This Data Science with Python Course includes 39 courses with 175 hours of video tutorials and One year access and several mock tests for practice. You will also get verifiable certificates (unique certification number and your unique URL) when you complete each of them. This training is for you to learn Python programming, statistics, machine learning algorithms and its application along with data visualization.
Offer ends in:
What you'll get
- 175 Hours
- 39 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
- Download Curriculum
Synopsis
- Courses: You get access to all 39 courses, in the Projects bundle. You do not need to purchase each course separately.
- Hours: 175 Video Hours
- Core Coverage: Data Science with Python, Artificial Intelligence with Python, Video Analytics Using OpenCV and Python Shells, Pandas with Python Tutorial, Machine Learning using Python, Statistics for Data Science using Python
- Course Validity: One year access
- Eligibility: Anyone serious about learning Data science using Python and wants to make a career in Data and analytics
- Pre-Requisites: Basic knowledge of Data Science and Python programming
- What do you get? Certificate of Completion for each of the 39 courses, Projects
- Certification Type: Course Completion Certificates
- Verifiable Certificates? Yes, you get verifiable certificates for each course with a unique link. These links can be included in your resume/LinkedIn profile to showcase your enhanced skills
- Type of Training: Video Course – Self-Paced Learning
Content
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Section 1: Building a Strong Foundation
Courses No. of Hours Certificates Details Machine Learning with Python 2024 5h 17m ✔ Machine Learning with Python Case Study - Covid19 Mask Detector 2h 05m ✔ Deep Learning: Automatic Image Captioning for Social Media with Tensorflow 2h 31m ✔ AI Machine Learning in Python 8h 37m ✔ Predictive Analytics and Modeling with Python 8h 26m ✔ Machine Learning using Python 3h 26m ✔ Data Science with Python Training 2024 11h 18m ✔ Matplotlib for Python Data Visualization - Beginners 4h 12m ✔ Matplotlib for Python Data Visualization - Intermediate 2h 53m ✔ Matplotlib for Python Data Visualization - Advanced 6h 37m ✔ Pandas with Python Tutorial 5h 42m ✔ NumPy and Pandas Python 5h 9m ✔ Pandas Python Case Study - Data Management for Retail Dataset 3h 22m ✔ Python Case Study - Sentiment Analysis 57m ✔ -
Section 2: Data Visualization and Advanced Python Libraries
Courses No. of Hours Certificates Details Seaborn Python - Beginners 2h 28m ✔ Seaborn Python - Intermediate 1h 18m ✔ Seaborn Python - Advanced 1h 56m ✔ PySpark Python - Beginners 2h 16m ✔ PySpark Python - Intermediate 2h 02m ✔ PySpark Python - Advanced 1h 18m ✔ -
Section 3: Exploring Artificial Intelligence and Advanced Topics
Courses No. of Hours Certificates Details Data Science with Python 4h 14m ✔ Artificial Intelligence with Python - Beginner Level 2h 51m ✔ Artificial Intelligence with Python - Intermediate Level 4h 2m ✔ AI Artificial Intelligence & Predictive Analysis with Python 6h 15m ✔ OpenCV for Beginners 2h 28m ✔ Video Analytics using OpenCV and Python Shells 2h 13m ✔ Statistics Essentials with Python 3h 23m ✔ Project on Tensorflow - Implementing Linear Model with Python 1h 46m ✔ Project - Data Analytics with Data Exploration Case Study 5h 6m ✔ Random Forest Algorithm using Python 1h 27m ✔ -
Section 4: Applying Machine Learning to Real-World Problems
Courses No. of Hours Certificates Details Python for Finance 1h 7m ✔ Financial Analytics with Python 1h 6m ✔ Linear Regression & Supervised Learning in Python 2h 28m ✔ House Price Prediction using Linear Regression in Python 3h 2m ✔ Logistic Regression & Supervised Machine Learning in Python 2h 6m ✔ Predicting Credit Default using Logistic Regression in Python 3h 3m ✔ Sales Forecasting using Time Series Analysis in Python 2h 13m ✔ Machine Learning Python Case Study - Diabetes Prediction 1h 02m ✔ Develop a Movie Recommendation Engine 51m ✔ -
Section 5: Assessing Your Skills with Mock Tests and Quizzes
Courses No. of Hours Certificates Details Test - Python Developer in 2022 Test - Python Developer 2022 Major 1 Test - Python Developer 2022 Major 2 Test - Python Game Developer Minor Test 1 Test - Python Game Developer Minor Test 2 Test - Python Game Developer Major Test
Description
Welcome to the comprehensive course on mastering machine learning with Python. In this course, you will embark on a journey to become proficient in one of the most sought-after skills in the modern tech industry: machine learning. Over the course of several sections, you will dive deep into various aspects of machine learning, from fundamental concepts to advanced techniques, all while harnessing the power of Python programming language.
Section 1: Building a Strong Foundation
In this section, we focus on establishing a solid understanding of machine learning fundamentals using Python. Through a series of courses and case studies, you'll explore key concepts such as supervised and unsupervised learning, data preprocessing, model evaluation, and more. Each course is designed to progressively build upon the previous one, starting with an introduction to machine learning with Python and gradually advancing to more complex topics like deep learning for image captioning. By the end of this section, you'll have acquired essential skills in machine learning algorithms and Python programming, setting the stage for more advanced learning.
Section 2: Data Visualization and Advanced Python Libraries
Section 2 expands your Python proficiency beyond machine learning by focusing on data visualization and advanced Python libraries. You'll learn how to effectively visualize data using libraries like Matplotlib and Seaborn, gaining insights into data patterns and trends. Additionally, you'll explore advanced data manipulation and analysis techniques using Pandas and NumPy. These skills are crucial for understanding and interpreting data, enabling you to make informed decisions and derive actionable insights.
Section 3: Exploring Artificial Intelligence and Advanced Topics
In this section, we delve deeper into artificial intelligence (AI) and advanced topics in Python. You'll explore cutting-edge AI concepts and applications, including computer vision with OpenCV and deep learning with TensorFlow. Advanced statistical analysis techniques are also covered, providing you with the tools to tackle complex AI projects and challenges. By the end of this section, you'll have a comprehensive understanding of AI principles and be well-equipped to apply them in real-world scenarios.
Section 4: Applying Machine Learning to Real-World Problems
Section 4 bridges theory with practice as you apply your machine learning knowledge to real-world problems and case studies. You'll work on projects such as predictive modeling, regression analysis, and recommendation systems, gaining hands-on experience in applying machine learning techniques to diverse domains. These projects not only reinforce your understanding of machine learning concepts but also enhance your problem-solving skills and prepare you for real-world applications.
Section 5: Assessing Your Skills with Mock Tests and Quizzes
The final section focuses on assessing your skills and knowledge through mock tests and quizzes. These assessments evaluate your understanding of machine learning concepts, Python programming, and data analysis. By completing mock tests and quizzes, you'll gauge your readiness for certification exams or real-world applications, ensuring you're well-prepared to tackle challenges in the field of machine learning.
Get ready to embark on an exciting journey into the world of machine learning with Python. By the end of this course, you will have the skills and confidence to tackle any machine learning challenge that comes your way. Let's get started!
Sample Certificate
Requirements
- Python Programming: Familiarity with Python programming is essential, including knowledge of basic syntax, data structures (lists, tuples, dictionaries), control flow (if statements, loops), functions, and modules. If you're new to Python, consider taking an introductory Python course or reviewing online tutorials.
- Mathematics Fundamentals: A solid understanding of foundational mathematics, including algebra, calculus, and statistics, will be beneficial. Concepts such as linear algebra (vectors, matrices), probability, and derivatives are often used in machine learning algorithms and data analysis. Reviewing these concepts beforehand can help you grasp the course material more effectively.
- Statistics: Knowledge of basic statistical concepts such as mean, median, mode, variance, and standard deviation is important for understanding machine learning algorithms and evaluating model performance. Consider reviewing introductory statistics materials if needed.
- Data Analysis: While not strictly required, prior experience with data analysis tools and techniques, such as working with datasets, data visualization, and data manipulation using libraries like Pandas, will be helpful. If you're new to data analysis, don't worry—this course will cover these topics in depth.
- Machine Learning Basics: Although not mandatory, having a basic understanding of machine learning concepts such as supervised learning, unsupervised learning, and model evaluation will be advantageous. You can familiarize yourself with these concepts through online resources or introductory machine learning courses.
- Curiosity and Persistence: Finally, a curious mindset and willingness to learn are perhaps the most important prerequisites. Machine learning and data science are vast fields with constantly evolving technologies, so being open to exploration and persistent in your studies will ensure success in mastering the material covered in this course.
Target Audience
- Aspiring Data Scientists: Individuals looking to enter the field of data science and gain practical skills in Python programming, machine learning, and AI.
- Software Developers: Programmers interested in expanding their skill set to include data analysis and machine learning techniques using Python.
- Students and Academics: Students studying computer science, statistics, engineering, or related fields who wish to deepen their understanding of data science and machine learning concepts.
- Professionals in Industry: Professionals from various industries seeking to enhance their analytical skills and harness the power of data for decision-making and problem-solving.
- Career Changers: Individuals transitioning to a career in data science or machine learning who require a comprehensive foundation in Python and related tools.
- Entrepreneurs and Business Owners: Business owners and entrepreneurs aiming to leverage data-driven insights and AI technologies to optimize business processes and gain a competitive edge.
Course Ratings
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I found this course very helpful. The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills
JIYEON CHOI
Very Informative and Well Organized Course Contents. High Quality Videos. I will recommend this course to anyone I know who interested to learn about Data Analytics.
JOSEPH WONG
I recently completed a data analytics course and found it to be an incredibly valuable learning experience. The course provided a comprehensive introduction to data analytics, covering everything from data collection and cleaning to advanced statistical analysis and data visualization. One thing I appreciated about the course was the hands-on approach to learning. Throughout the course, we worked with real datasets and used industry-standard tools such as Python, R, and Tableau to analyze and visualize the data. This gave me the practical skills and experience I needed to feel confident in my ability to work with data in a professional setting. The course instructors were knowledgeable and engaging, and they were always available to answer questions and provide feedback. The course also had a supportive and active online community, where I was able to connect with other learners and share my experiences and insights. Overall, I would highly recommend this data analytics course to an
Akram Ahmed
The Data Science Fundamentals online course that I recently completed. Overall, I found the course to be highly valuable and informative. The content was well-structured and provided a solid foundation for understanding key concepts in data science.
Priti Gajanan Patole