Do you often wonder, how exactly it is that Google ALWAYS shows you the correct search results for all that you want to know? What is in that Google Algorithm?
If yes, this course is for you because Machine Learning is the key behind it.This introductory computer science course in machine learning will cover basic theory, algorithms, and applications and teach you the importance of applying predictive models in fields like finance, education, healthcare and more.
Time flexibility & Individualized attention
The batch size is not more than 8 allowing the mentor to spend considerable amount of time guiding and improving your competence.
Project Based Curriculum
Project after each milestone to elevate your coding skills and reflect upon the concepts taught.
Mentor-led,to keep you on track
Real-time, functional applications created under the guidance of a mentor, based on Industry practices.
Mastery in Problem Solving Skills
Course designed with a view to focus on programming logic and strengthen the fundamentals.
Holistic Programs designed to build your Career in IT
Being a jedi-coder is language-agnostic. Passion for programming does not emerge from learning countless languages, it is the outcome of evolved thinking.
Since 2012, we at Swabhav Techlabs work everyday to bring together passionate, experienced instructors and driven students to inspire and learn from one anothe through an immersive learning experience focused on improving the problem solving skills and thinking capability of each individual
We’re backed by our outcomes, our near-perfect employment rate for 300+ job-seeking students in the Industry.
Swabhav classes are designed to be highly selective and diverse to bring out the best in the students.
Our 12-18 week programs are intensive and rigorous, giving our students the skills needed to become well-rounded, modern software engineers.
Prior to entering the Machine Learning Engineer Nanodegree program, the student should have the following knowledge:
Intermediate Python programming knowledge
- Strings, numbers, and variables
- Statements, operators, and expressions
- Lists, tuples, and dictionaries
- Conditions, loops
- Procedures, objects, modules, and libraries
- Troubleshooting and debugging
- Research & documentation
- Problem solving
- Algorithms and data structures
Intermediate statistical knowledge
- Populations, samples
- Mean, median, mode
- Standard error
- Variation, standard deviations
- Normal distribution
- Precision and accuracy
Intermediate calculus and linear algebra mastery
- Series expansions
- Matrix operations through eigenvectors and eigenvalues
LEARNING AT SWABHAV
- Python installation
- Data types in python
- Basic operations and functions
Numerical Computing Using Python
- Introducing numpy
- Vector matrix arrays
- Plotting and visualization
- Scipy Toolkit
Introduction to Pandas
- Numpy for pandas
- Playing around pandas series
- Playing around pandas data frame
- Working on dataset In pandas
- Combining reshaping data
- Grouping and aggregating data
Basics of Machine Learning
- Definition of machine learning
- Types of machine learning
- Few examples on machine learning implementations
- Machine learning: the problem setting
- Loading an example dataset
- Learning and predicting
- Model persistence
ADMISSION & PROGRAM DETAILS
Make the Jump
When we say we build a community, we genuinely do. We dont just select an individual student but rather cultivate a group of diverse and unique people with passion for technology.
- 1. Register yourself here
- 2. Get Invited to Interview
- 3. Complete Technical Application and pass a Code Assessment.
- 4. Receive Conditional Acceptance. Enroll & submit a deposit within 7 days to secure your spot.
What we look for:
We love programming and hence look for people who have an equal passion by learning on their own. They have the determination to become a developer regardless of the circumstances.
Swabhav Techlabs Students are smart, hardworking and focused. They are quick learners with a constant need to know more of whats out there in technology
We look for students who are considerate, understanding, helpful and generally pleasant to be around.