Detailed Course Curriculum
Lesson by lesson
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Instructions for course access
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Inter onboarding meeting
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How to use Discussions option
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Course access duration
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Piracy & infringement warning
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1_Introduction
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2_What is Python ?
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3_Is Python really needed ?
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4_Installation
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5_Jupyter Notebook
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6_Your First Code
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7_Lab: Your First Code
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8_Data types
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9_Lab: Data types
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10_Operations
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11_Lab: Operations
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12_Type Conversion
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13_ Lab: Type Conversion
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15_Lab: Input statement
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14_Input statement
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16_IF
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17_else
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18_Lab: If else
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19_While
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20_ break and continue
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21_Lab: While, break and continue
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22_Functions
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23_Lab: Function
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24_for loop
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25_Lab: for loop
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26_Practice
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Week 1 feedback
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1_List
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2_Lab: List
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3_Slicing
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4_Lab: Slicing
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5_Dictionaries
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6_Lab: Dictionaries
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7_Tuples
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8_Lab: Tuples
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9_Exceptions
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10_Module, Package & Library
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11_Lab: Numpy Part: 1
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12_Lab: Numpy Part: 2
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13_Lab: Numpy Part: 3
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14_Lab: Numpy Part: 4
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Dataset for Pandas
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15_Lab: Pandas Part: 1
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16_Lab: Pandas Part: 2
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17_Lab: Matplotlib
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18_pip
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Week 2 feedback
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1. What is Machine Learning ?
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2. Applications of Machine Learning
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3. Steps Involved in Machine Learning
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4. Types in Machine Learning
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5. Types in Machine Learning in Detail
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6. Flow of Machine Learning
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7. Getting Started
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8. Theory: Linear Regression
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9. Lab: Mathematical Approach vs Library Approach
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Dataset for Linear Regression Project
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10. Lab: Linear Regression Project
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11. Types of Curve Fittings
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Week 3 feedback
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1. Theory: Logistic Regression
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Dataset for Logistic Regression Project
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2. Lab: Logistic Regression Project
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3. Theory: Decision Tree
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4. Getting Ready for Decision Tree
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Dataset for Decision Tree Project
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5. Lab: Decision Tree Project
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6. Theory: Random Forest
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Dataset for Random Forest Project
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7. Lab: Random Forest Project
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8. Euclidean Distance
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9. Theory: K- Nearest Neighbor
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Dataset for K- Nearest Neighbor Project
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10. Lab: K- Nearest Neighbor Project
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11. Theory: K Means
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Dataset for K Means Project
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12. Lab: K Means Project
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Week 4 feedback
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Instructions to generate your certificate
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Leverage your certificate to get Internship or Job
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![](https://import.cdn.thinkific.com/212772/courses/1062619/MjoyNyt6RwGtqb5OmFTy_Machine%20Learning%20with%20Python%20Crash%20Course-01.jpg)
About this course
- 85 lessons
- 17.5 hours of video content
- Mentor guided projects
- 5 course rating & 3500+ learners
Software tools used
About the Course
The application of Machine Learning (ML) is found in all the engineering streams & sectors today. There are over million+ job opportunities available in the areas of ML & are expected to increase in the future. Irrespective of your background in engineering, having subject exposure to ML can improve your analytical skills, ability to optimize the system, problem-solving & better chances to find a career.
This 4-week online course on ML with Python is outlined to enable learning possibilities for everyone. The content of this course is well organized, easy to learn, and structured with 7 projects. By the end of 4 weeks, you will have the confidence to code in Python, learn to use 3+ libraries, understand the math behind ML for supervised & unsupervised learning, and you will transform the theoretical knowledge into practical skills with projects & assignments.
Start with Python Fundamentals & motivation to master Python
Week 1
Practice with examples & gain confidence to start projects
Week 2
Start to build amazing realtime projects & gain experience
Week 3
![](https://import.cdn.thinkific.com/212772/custom_site_themes/357759/og3l4lySnqCYDhncIwOB_1.png)
Major project, attention to detail & mastery
Week 4
![](https://import.cdn.thinkific.com/212772/custom_site_themes/357759/zHufEfTfTNKuU3XGarZ4_2.png)
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Machine Learning with Python Crash Course
₹2,999.00
Buy Now -
Reach us for any queries - 7411019255 / [email protected]
Sample Certificate
![](https://import.cdn.thinkific.com/212772%2Fcustom_site_themes%2Fid%2FmGnSAdNR6i9fLp3cZktO_lms.decibelslab.com%20%2810%29.png)
Participants posts on LinkedIn
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Frequently Asked Questions
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Should I have any prerequisite knowledge to learn this course?
The course will be taught from fundamentals with real-world examples & hands-on sessions. It should not be a concern if you do not have prior exposure to programming/software used.
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Should I need a computer?
Yes, you will need a computer to practice.
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How the teaching will happen?
You will get full access to the course upon the completion of payment.
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What should I do, if I get a doubt?
You need to report the doubts/issues over the Discussion box available in the course. The course coordinator will clarify your doubts by replying via the discussion box. If needed, we will arrange meetings with the mentor, to clarify your doubts via Zoom.
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How many projects I will work on?
You will work on 7 hands-on minor & major Projects
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How will I build projects?
Projects will be guided by the mentor with the help of recorded lectures.
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How much time do I have to spend everyday?
We suggest 2 hours per day, an average of 10 hours per week.