Data Science and Machine Learning Course


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


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


  • 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