Data Science and Machine Learning Course

HANDS-ON INDUSTRY TRAINING ON DATA SCIENCE MACHINE LEARNING FROM XCEED ACADEMY

Get a holistic data machine learning course from Xceed Academy and bolster your career in data science without any hiccups. At Xceed Academy, our data science machine learning program is a leap forward from statistics, computer science, and other emerging applications in the industry. We provide structured course pathways that leverage the latest technology for developing efficient and fast algorithms along with data-driven models.

Believe it or not, but the availability of more data invariably and implicitly means bringing in new predictive models that work accurately. Conventional statistical solutions are focused on static analysis, but our data science machine learning programs emphasize dynamic analysis that gives rise to reliable and accurate conclusions.

Who can opt for our data science machine learning training programs?

A bewildering range of people can benefit from our data science machine learning programs. Some examples are listed as follows in the below section:

• Data analysts, data scientists, and other professionals who want to turn massive volumes of data into actionable insights

• Those who want academic or professional training in applied statistics or mathematics.

• The early-career professionals as well as senior managers including the business intelligence analysts, technical managers, IT practitioners, business managers, and management consultants.

What can you expect to learn?

With our data science machine learning courses, you can learn:

• The fundamentals of machine learning

• Ways to build a functional recommendation system

• Ways to perform cross-validation and to avoid overtraining

• A wide array of popular ML algorithms

• Regression and Prediction fundamentals along with classification and hypothesis testing

Data science machine learning with Xceed Academy

• Ranging from the foundational concepts of data science that lay the cornerstone of analysis to the advanced intricacies, we teach everything. With expert guidance from our seasoned mentors, you can learn Python, statistics for data science, and more.

• Our experts help newbies to understand more about bootstrapping data to make it ML/AI-ready along with the practical applications of the taught techniques. This is achieved by teaching students the basics of components, spectral clustering, clustering, and Embeddings.

• The next emphasis is on classic linear as well as non-linear regressions and extensions. We explore the basics of supervising ML and prediction along with key algorithms and prevalent topics like modern regression and high-dimensional data.

• We also help our trainees to discover prime avenues of data science that are applicable in pragmatic fields through hypothesis testing and deep learning.

Why Choose Xceed Academy?

By following a pragmatic pedagogy in Data Science and Machine Learning, we facilitate individuals and organizations to construct a diverse understanding through real-world case studies and vivid practicing activities. With our data science machine learning training programs, enterprises, and individuals can experience new levels of growth. Irrespective of whether you are seeking continuous business growth through big data, or you want more, our basic and advanced-level training can give you hands-on experience in highly-sought-after ML skills.

Do you want to find out more about data science machine learning? Enroll now and get started with Xceed Academy!

Course Duration: 5 Days

Prerequisites

Knowledge of Python programming
Able to munge, analyze, and visualize data in Python

Highlights

40 hours of Instructor Led Trainings
Hand-on Exercises on Live Projects
Practice Labs
End of Training Project
Module-wise Auto-graded Assessments and Quizzes
90 days exclusive post training online mentorship

Module 01

Introduction to Data Science

Introduction to using data in 2021
The 5 stages of the data science process and the four flavors of data analysis
Why choose Python (differences between Python and R)
Installing Python on our machine and using the terminal
Installing Visual Studio Code
Getting started with Python
Python for Data Engineering
R for Data Analysis
How Machine learning & Deep Learning works
How Deep learning neural networks Big data work
Understanding Predictive analytics and Prescriptive analytics
Business intelligence with data science using Sources of Data, Data Preparation, In-house data, Open data and APIs
Overview of Scraping data and Creating data
Passive collection of training data and Self-generated data

Module 02

Learning Python

Definition of Algorithms Definition of Data Structures
Hello world, Expressions and Statements
Blocks and scope & Conditionals
Functions and Objects
Variables and Data Types
Lists & Loops
Dictionaries
Frequency Tables
Algorithm Practice
Solving Algorithms
Basics of Data Science to Solve Problems
Getting answers to our data questions (the 5 stages of Data Science in practice)

Module 03

Data Structures

Tools for Data Science, Applications for data analysis and Languages for data science
Machine learning as a service, Data Visualization 101 and The three types of data visualization
Selecting optimal data graphics, Communicating with color and context Analyses for Data Science
Descriptive analyses, Predictive models and Trend analysis
Clustering, Classifying and Anomaly detection
Dimensionality reduction, Feature selection and creation, Validating models and Aggregating models
Data Preparation Basics Filtering and selecting
Treating missing values Removing duplicates Concatenating and transforming
Grouping and aggregation & Conditional Statemenets
Structured Data, Classes, Exceptions and String Objects

Module 04

Data Collection & Web Scrapping

The data collection process
Getting data in
- Internal (Databases, CSV’s, etc.)
- External (API’s, Web Scraping, 3rd Party services)
Practical Data Science Applications: Getting Data in using web scraping
How data scraping works on the web, Traversing the DOM, finding elements by class and ID & Avoiding detection when scraping
Mocking inputs / Pagination – Search and filters
Using sitemaps and robots.txt files & Error Handling
Saving, reading / writing to a file
Extracting data
Data Sourcing via Web Scraping
BeautifulSoup&NavigableString
Data parsing
Introduction to NLP (Natural language processing)
Cleaning and stemming textual data
Lemmatizing and analyzing textual data

Module 05

Analysis and Visualization

Descriptive analyses and Predictive models
Regression, Clustering and Classifying
Anomaly detection, Dimensionality reduction, Feature selection and creation
Validating models and Aggregating models
Mathematics for Data Science: Algebra & Calculus
Optimization and the combinatorial explosion and Bayes’ theorem
Acting on Data Science, Interpretability Actionable insights
Legal, ethical, and social issues of data science + Agency of algorithms and decision-makers
Collaborative Analytics with Plotly
Create statistical charts Line charts, Bar charts and pie charts
Opening files Text vs. binary mode
Using standard modules and Creating a module
Practical Data Visualization
Creating standard data graphics, Defining elements of a plot, Visualizing time series, Creating statistical data graphics

Module 06

Data Engineering

Data Engineering Introduction
Definition of Big Data
Tools in Big Data
Overview of Apache Spark, Algorithms in Big Data
Scientific Python Overview
Ramp up with Scientific Python
Overview of Jupyter Notebooks & Overview of DataBricks
Start the notebook server, Use code cells, Extensions to Python language
Understand markdown cells, Edit notebooks
NumPy Basics Overview: NumPy NumPy arrays
Slicing Learn Boolean indexing
Understand broadcasting, Understand array operations, Understand ufuncs
Pandas overview, Load CSV files, Parse time
Use pure Python packages, Calculate speed, Display a speed box plot, Conda Overview

Module 07

Machine Learning

Main Components of Machine Learning, Prediction (with Linear Regression)
Dimensionality Reduction (with Principal Component Analysis) & Density Estimation (with Gaussian Mixture Models)
Classification (with Support Vector Machine)
Programming & Mathematical Foundations
Fundamental Concepts of Machine Learning
Classification, Clustering, and Regression
Supervised Learning & Unsupervised Learning
Reinforcement Learning, Train/test and cross-validation & Accuracy metrics (RMSE and MAE)
Top-N hit rate: Many ways Coverage, diversity, and novelty
Churn, responsiveness, and A/B tests Review ways to measure your recommender Our recommender engine architecture K-nearest neighbors (KNN) and content recs
Deep learning introduction & Deep learning prerequisites
Basics of artificial neural networks, Introduction to TensorFlow and Introduction to Keras, CNN architectures & Intro to deep learning for recommenders, Restricted Boltzmann machines (RBMs), Recommendations with RBMs