Data Science, Machine Learning & AI Course | FC Training

Data Science, Machine Learning, and AI Course

Our IT courses are constructed keeping in mind the jobs available in the IT market, by making our students work on actual data. These courses offer complete working knowledge of IT domain, making you think like a developer, with all essential concepts taught in universities with years of practical tips and working strategies included. Our courses prepare our students to tackle more than required tasks asked by companies in their job descriptions making you highly employable. Once our students complete one of these courses, they can work on any similar tool that might come out in the future depending upon the type of course they have undertaken.

Course Overview

The data science course provides a comprehensive overview of essential topics including Mathematics, SQL, Python, and Machine Learning. Students will learn mathematical concepts such as probability, statistics, and linear algebra, which are critical in data analysis. In addition, they will develop skills in SQL, a programming language used for managing and manipulating databases, which is vital for data retrieval and storage. Students will also learn Python, a versatile programming language used extensively in data science, to write code and develop analytical models. Lastly, students will gain expertise in Machine Learning, a branch of Artificial Intelligence (AI) that enables systems to learn from data and improve over time, which is crucial in predictive modeling and pattern recognition. Overall, the data science course aims to equip students with the knowledge and skills required to succeed in the rapidly growing field of data science.

Suitable For

This course is suitable for

Individuals interested in learning about data analysis and machine learning.

Beginners who are looking to gain a strong foundation in essential topics such as mathematics, programming, and machine learning.

Those with some prior experience in the field who want to enhance their skills and knowledge.

Students who want to develop analytical models and make data-driven decisions.


Certification in Data Science, Machine Learning, and AI

Course Modules

Mathematics and statistics are crucial in machine learning, providing tools for creating models that learn from data and make accurate predictions. Linear algebra, calculus, and probability theory are used in algorithms, while statistics analyzes data, identifies patterns, and predicts outcomes. Strong knowledge in these areas is necessary for success in machine learning.

1. Mathematics

  • Linear Functions
  • Linear Algebra
  • Vectors
  • Matrices
  • Tensors

2. Statistics

  • Descriptive
  • Variability
  • Distribution
  • Probability

SQL is critical in machine learning, allowing efficient data retrieval and analysis from relational databases. It ensures accurate analysis of large data sets, with solid understanding essential for success.

  • DDL and DML in MySQL and setup
  • ERD Diagrams and Relational Mapping
  • Data normalisation
  • Basic Queries
  • Database Manipulation
  • Table Manipulation
  • Relational Algebra
  • Advanced SQL - Joining, Subquery, Views
  • Database Security
  • Multiple Activities to Perform

Python is a widely-used programming language in machine learning. It offers powerful libraries, tools, and frameworks for data analysis, modeling, and visualization. Python's simplicity and flexibility make it a popular choice for building machine learning models and deploying them in real-world applications.

  • Python Setup and What is Python?
  • Data Types and Syntax
  • Comparison Operators
  • Python Loop
  • Python Statements
  • Logical Operators
  • Methods and Functions
  • Error and Exception Handling
  • Modules Packages and libraries
  • Debugging
  • Advanced python Modules (DateTime)
  • File Management
  • Multiple Activities to Perform
  • Multiple Projects to Build

Machine learning is a subset of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It involves the use of algorithms, statistical models, and data to build predictive models and uncover insights from large datasets.

  • Data Preprocessing

Supervised Learning

  • Regression Models
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Random Forest Regression
    • Topics such as
      • Assessing a Regression Model
      • Bias vs Variance
      • Regularisation
      • Gradient Descent
  • Classification Models
    • Decision Tree Classification
    • K-Nearest Neighbor
    • Logistic Regression
    • Naïve Bayes
    • Random Forest Classification
    • Support Vector Machines
    • Additional Topics
      • Assessing a Classification Model
      • Adaboost
      • Gradient Boosting
      • XGBoost
      • Grid Search CV

Unsupervised Learning

  • Clustering Models
    • Hierarchical
    • K-Means Clustering
  • Association
    • Apriori
    • Eclat
  • Build Dashboards for Data Visualisation
  • Solved Sample Code Files for easy practice
  • Access to Multiple Datasets
  • 7+ Real world data projects
  • Reinforcement Learning
    • Thompson Sampling
    • Upper Confidence Bound (UCB)
    • Q-Learning
  • Natural Language Processing (NLP)
  • Deep Learning
    • Artificial Neural Networks (ANN)
    • Convolutional Neural Networks (CNN)
    • Recurrent Neural Networks (RNN)
    • Additional Topic
      • LSTM
  • Solved Sample Code Files for easy practice
  • Access to Multiple Datasets
  • 2+ Master A.I. Projects
ADDON Module:

Learn the fundamentals of version control via git and create your amazing looking developer portfolio on github to showcase your code or share your code with the world.

Learn to create custom custom graphical user interface applications using one of the widely used library, TkInter. You will also learn to use the inbuild database within python, SQLite.

Skills You Will Gain

The students will acquire following set of skills after completing this Training Course.

Ability to use Mathematical Concepts

Proficiency in SQL

Become an expert in Python

Expertise in Machine Learning

Ability to Analyze and Visualize Data

Data Cleaning and Preprocessing Techniques

Machine Learning Algorithms

Artificial Intelligence Algorithams

Data ethics and Data management and Analysis

Career Path

Data Analyst

Machine Learning Engineer

Data Scientist

Python Developer

Database Administrator

Machine Learning Projects

Weather Prediction (Time Series Data)

Predicting cancer malignant or benign based on Data

Predicting Stock Prices

Hand Written Digit Recognition

Wine Quality Prediction

Marketing Data Analysis

Taxi Fare Prediction

Master Projects

A project on Reinforcement Learning to display your practical skills to the RECRUITER.

A project on Reinforcement Learning, Deep Learning and Natural Language Processing (NLP) Learning to display your practical skills to the RECRUITER.

A project on Large Language Model (LLM) to generate image from a prompt given (Text-to-Image) and to generate the description of the image from a given image (Image-to-Text).

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Data Science Course

This course includes:
  • Learn Python, SQL (Database)
  • Learn Data Science and Machine Learning Skills
  • Learn some popular software such as VS Code, XAMPP/MAMPP, Anaconda
  • Learn some popular libraries such as ScikitLearn and TensorFlow(Keras)
  • Creative Activities, Lectures and more
  • Masters level Course Content with Hands-on Training
  • Multiple Projects to test your skills upon
  • Join the leading Industry with over 20 billion Pounds of investment
  • Online Practical Training
  • Flexible Payment Structure
  • CV Support

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