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.
Data Science With Python
Ace Data Science
Data Science is one of the hottest fields of the 21st century. It is in high demand across the globe with bigwigs like Amazon, Google, Microsoft paying handsome salaries and perks to data scientists. This course provides you with a great kick-start in your data science journey.
Starting with Python Basics, Data Visualization, Data Scraping, Building Web Scrappers using Scrapy, Data Cleaning and applying various machine learning algorithms like Linear Regression, Logistic Regression, Decision Trees, Naive Bayes, Principal Component Analysis, Feature Engineering, T-SNE Visualizations, Deep Learning & Reinforcement Learning for video games. The complete course is very practical and makes sure you do sufficient hands-on practice on the concepts taught by solving assignments and challenges
Start from scratch
Begins with Introduction to Python and do not requires to have prior understanding of python and machine learning. Course will create good foundation for becoming data science engineer.
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.
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
Many of the topics are advanced, up-to-date and require great patience and coding skills.
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
- OOPS and Modules
- File Handling in Python
Mathematical Concepts and Data Visualisation
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
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
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
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
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
|Center||Start Date||End Date||Day & Time||Batch Type|
|Noida||24th Dec||TBD||Tue (12:15 PM - 3:45 PM), Thu (12:15 PM - 3:45 PM), Sat (12:15 PM - 3:45 PM), Sun (12:15 PM - 3:45 PM)||Noon|
(Drop a line at firstname.lastname@example.org if you have further queries)
Who should do this course?
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 our students say
★ ★ ★ ★ ★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.
★ ★ ★ ★ ★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.
★ ★ ★ ★ ★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.
★ ★ ★ ★ ★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.