This field intensively deals with mathematics, for analysis of data and algorithms. A decent mathematical background is a necessary and we engineers definitely excel at that! If you have had a mathematical background at school and covered engineering mathematics (such as probability, statistics, linear algebra etc/), you're good to go! Maybe you could just brush up the topics.
Machine Learning
Advanced Machine Learning & AI Course
Overview
Machine learning is a field of study that helps machines to learn without being explicitly programmed. Machine Learning has become the hottest computer science topic of 21st century. All big giants such as Google, Microsoft, Apple, Amazon are working on ML projects and research organizations such as NASA, ISRO invest heavily in R&D for ML projects.
Being an advanced course, students are required to have clear understanding of programing fundamentals and knowledge of Python language will be helpful. In this course, students will follow industry-standard programming practices to build intelligent systems, working on AI algorithms and data crunching
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Implement Algorithms
Machine Learning is an hands-on coding course. You will learn various supervised and unsupervised learning algorithms and will implement them in your code from scratch.
Work on LIVE Projects
This course covers a lot of basic & advanced projects like face-recognition, sentiment analysis, recommendation system, coversational engine, AI music generator and much more.
Deep Learning
We will cover the latest advanced in deep learning - a growing field in Machine Learning.Deep learning applications are being used in computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics
Advanced Topics
Many of the topics are advanced, up-to-date and require great patience and coding skills. Make sure you have good programming fundamentals and practice of Python before joining this course.
GET FREE ACCESS TO ONLINE COURSE
Our Online courses are an exact replica of our Classroom programs. Once a student completes their course, we provide them access to the Online course as well to make sure they can revise at their will. Each Online course comes with Mentor support Hands on coding assignments, exclusive access to our online IDE, Hackerblocks & Doubt resolution;
Course Contents
Python Introduction
Lectures 1-5
Course will start with basic understanding of logic. We will be discussing about Flowcharts, PsudoCode and students will solve also some of the Puzzles in initial classes.
- Data Science Quickstart Mode
- Python 3.7 overview
- Python Basics
- Control Flow
- Data Structures
- Functions
- OOPS and Modules
- File Handling in Python
Mathematical Concepts and Data Visualisation
Lecture: 6-10
Data visualization is about the presentation of data in a pictorial or graphical format. Further mininga and warehouses is key to have successful data science application.
- Linear Algebra
- Data Visualisation
- Probability Distribution & Statistics
- Numpy, Scipy, and Scientific computation with Python
- Data Analysis using Pandas
- Data Acquisition - Web Scrapping, Web APIs
- Data Scraping, Handling, Cleaning
- Importing/Exporting Data from/to various sources
- Identify missing/outliers data
- Normalizing and Formatting data
Machine Learning Algorithms
Lecture 11-15
Simple to Advanced Machine Learning Algorithms. Includes all useful traditional supervised and unsupervised algorithms.
- K-Nearest Neighbour search
- K-means clustering
- Linear Regression
- Logistic Regression
- Decision trees and Naive Bayes
- Random Forest Classifiers
- Manual Ensembling Vs. Automated Ensembling
- Bagging, Boosting of trees
- Ada Boost and XGBoost
- Support Vector Machines
- SVD and Principal Component Analysis
Segmentation and Time Series
Lecture 16-20
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data.
- Introduction to Segmentation
- Subjective Vs Objective, Heuristic Vs. Statistical
- Heuristic, Behavioral Segmentation Techniques
- Hierarchical Clustering vs Spectral Clustering
- Feature selection and importance
- Principle component Analysis (PCA)
- Time Series - Components
- Time Series - Averages, Smoothening, AR Models
- Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc
Deep Learning
Lectures 21-24
In this final classes of our course, we will learn about more complex Machine Learning topics and algorithms which help you in solving and optimising solutions of lots of real world problems.
- Neural Architectures and Training
- Deep learning methods
- Convolutions and the GoogLe Net
- Dimensions revisited: The Auto-encoder
- Recurrent and Combined Architectures
- Transfer Learning
Projects and case studies
Across Lectures
Students will get hands on knowledge of the underlying material by building several projects that use the techniques taught in this course to solve a real life problem.
- Face Recognition
- Image classification and Object detection
- Text/Poem generating bot
- Recommender systems
- PokeMon Classification
- Web Crawler
- Cartpole Player
- Dominant Color Extraction
- Fashion Retailing Forecasting
Course Schedule
| Center | Start Date | End Date | Day & Time | Batch Type | |
|---|---|---|---|---|---|
| Pitampura | 4th June | TBD | Thu (12:10 PM - 3:50 PM), Sun (12:10 PM - 3:50 PM), Tue (12:10 PM - 3:50 PM) | Normal Noon | |
| Pitampura | 7th June | TBD | Sun (8:00 AM - 11:50 AM), Sat (8:00 AM - 11:50 AM) | Weekend Morning | |
| Pitampura | 11th June | TBD | Thu (4:10 PM - 7:50 PM), Sun (4:10 PM - 7:50 PM), Mon (4:10 PM - 7:50 PM) | Normal Evening | |
| Noida | 20th May | TBD | Mon (9:00 AM - 12:00 PM), Wed(9:00 AM - 12:00 PM), Fri(9:00 AM - 12:00 PM), Sat(9:00 AM - 12:00 PM) | Fast Track Morning | |
| Noida | 5th June | TBD | Sat(12:30 PM - 03:30 PM), Sun(12:30 PM - 03:30 PM) | Normal Noon | |
| Noida | 9th June | TBD | Tue(04:00 PM - 07:00 PM), Thurs(04:00 PM - 07:00 PM), Sat(04:00 PM - 07:00 PM), Sun(04:00 PM - 07:00 PM) | Normal Evening | |
| Dwarka | 9th June | TBD | Tue (10:00 AM - 2:00 PM), Thu (10:00 AM - 2:00 PM), Sat (10:00 AM - 2:00 PM) | Normal Morning |
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FAQ
(Drop a line at admissions@codingblocks.com if you have further queries)
Who should do this course?
Why do this course?
Machine learning evolves from artificial intelligence and study of pattern recognition. Today, when excessively huge amounts of data are being dealt with everyday, rather every moment, pattern recognition is something that helps large corporations and websites work magnificently with the users. Artificial intelligence has become a favourite with the customers, esp. intelligent personal assistants like Apple's Siri, Microsoft Cortana, etc. We hope to extend our knowledge to the students so they are ready to tackle such problems in real-world.
I know competitive programming. should I do this course?
Machine Learning requires extensive knowledge of mathematics, which you also gain while handling problems in competitive programming. This course shall equip with the right tools to handle huge amounts of data and derive meaningful conclusions from data crunching. Handling real-world problems is exactly what machine learning is all about, a skill very useful in development tasks. competitive programming equips you with critical thinking, but machine learning teaches you how to apply that skill and solve complex problems.
Will this help me in interviews?
Machine Learning and Data science has been called as the ‘Hottest job of the 21st century’. If you learn this course well, you’ll be able to impress quite a lot of interviewers across various interviews.
I don’t pass all the prerequisite criteria. Should I enroll?
Even if you don’t possess understanding of all the prerequisites, we shall help you cover every topic in detail and provide overview before diving deep into machine learning and data science. Python is a relatively easy language to learn, and you can pick up the basics very quickly. Therefore, you’ll have ample amount of time before the course to brush-up/learn the fundamentals.
What is asked in admission test for this course?
You are suggested to revise programming fundamentals, OOPS concepts, basics Maths and Probability concepts, hands-on coding in Python before giving the admission test for Machine Learning course.
What our students say
Kartik Aggarwal
ML
★ ★ ★ ★ ★Review:The instructor and the teaching assistants were amazing and highly experienced. Side by side projects every week made the topics even more understandable and interesting. Thus I would highly recommend this course to everyone.
Ishpreet Singh
ML
★ ★ ★ ★ ★Review:This was my first complete course at coding blocks. I attended the machine learning bootcamp in Winter 2016 and loved it which made me join the full course. Shubham bhaiya and Ayush bhaiya(TA) have good knowledge about the field and were very helpful in making us understand the concepts. I would certainly recommend this course to anyone who has interest in Data science.
Anshul Garg
ML
★ ★ ★ ★ ★Review:Had an amazing experience here at Coding Blocks. The course content is excellent and structured well. The emphasis is not only on the theory part but also on logical thinking and how to approach a problem. Doubts were taken very well.
Shivanshu Chaudhary
ML
★ ★ ★ ★ ★Review:A well curated course which is capable of creating a strong base and giving initial impetus to go deeper in the NET ;) .A nice balance is maintained between industrial applications and research aptitude.
