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Key Highlights

G-Tec Jain Education Data Science & AI training
Data Science & AI training
240 hours of intensive, industry-aligned Data Science & AI training
G-Tec Jain Education Series covered end-to-end
Series covered end-to-end
Python, ML, Deep Learning, NLP, SQL & Time Series covered end-to-end
G-Tec Jain Education Hands on Learning
Hands on Learning
100% hands-on learning with real business datasets and live projects
G-Tec Jain Education Certified by NSDC
Certified by NSDC
Certified by NSDC, G-TEC JAIN Keerti & JAINX University
G-Tec Jain Education Career Mentoring
Career Mentoring
Job assistance with interview preparation and career mentoring
G-Tec Jain Education Flexible Schedules
Flexible Schedules
Flexible schedules with expert faculty and industry tools exposure

About the Course

G-Tec Jain Education Gain essential skills in data analysis
Industry-Relevant Course

Focus on acquiring skills that are in high demand across various sectors. This course is designed with input from industry professionals to ensure it meets current market needs.

G-Tec Jain Education Hands-On Learning
Hands-On Learning

Emphasize practical, hands-on learning through projects and case studies that simulate real-world problems, allowing you to apply theoretical knowledge in practical scenarios.

G-Tec Jain Education Cutting-Edge Technologiesy
Cutting-Edge Technologies

Stay ahead of the curve by learning the latest tools and techniques in AI and ML, positioning yourself as a leader in the field.

G-Tec Jain Education Expert Mentorship
Expert Mentorship

Benefit from the guidance of seasoned professionals who bring real-world experience to the classroom, helping you bridge the gap between theory and practice

G-Tec Jain Education Holistic Learning Experience
Holistic Learning Experience

Engage in an interactive learning environment that combines video lectures, live sessions, peer discussions, and one-on-one mentoring.

G-Tec Jain Education Career prospects
Strong Placement Support

Leverage our extensive network of industry connections and strong placement support to secure roles in leading organizations, ensuring that your education translates into a successful career.

Content

Data Science with Python, Artificial Intelligence and Machine Learning, is an intensive 240-hour course, focused on equipping participants with practical skills in Python, AI & ML. The course includes hands-on projects, ethical considerations and communication training, preparing Graduates for roles such as Data Scientist or Machine Learning Engineer

  • Introductions to Data Science
  • Domains in Data Science
  • Need of Data Science
  • Use of Data Science in Business
  • Lifecycle of Data Science Projects
  • Data Science Tools and Technologies
  • Basics of Excel for Analysis
  • Required Skill for Data Science

  • Types of data
  • Descriptive vs Inferential Statistics
  • Sampling Techniques
  • Measures of Central Tendency and Dispersion
  • Hypothesis & Inferences Testing
      • 1 . F Test
      2 . T Test
      3 . ANNOVA
      4 . Chi Square Test
  • Confidence Interval
  • Central Limit Theorem
  • P value
  • Variables
  • CoVariance and Corelation

Supervised

  • Linear Regression / Multi-Linear Regression
  • Logistic Regression
  • Gradient Descent
  • Decision Tree (CART)
  • Random forest (Ensemble Learning) // Boosting /Bagging
  • K Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Naive Bayes Classifier (NBC)
  • GRID SEARCH CV AND RANDOM SEARCG CV

Unsupervised

  • Hierarchical Clustering / Dendrograms
  • K Means Clustering
  • DBSCAN
  • MINI BATCH K MEANS

Dimension Reduction Models

  • PCA
  • Kernal PCA

TIME SERIES ANALYSIS

  • ARIMA
  • FB PROPHET

AI

  • ANN
  • Simple ANN Model

CNN

  • Transfer Learning (VGG16 / VGG 19 / RESNET 50 / Inception V3)

NLP

  • 2 BAG of words(count vectorization)
  • 3 TD-IDF-term frequency inverse document frequency
  • Introduction to Python
  • Command line basics
  • Numbers, Operators & Comments
  • Variables & Strings
  • Boolean & Conditional Logic
  • Looping in Python
  • Lists
  • Dictionaries
  • Tuples & sets
  • Functions & Adv Functions
  • Modules
  • File I/O

  • Introduction to NumPy and Creating NumPy Arrays
  • Basic Operations on Arrays
  • Indexing and Slicing
  • Reshaping, Stacking, and Splitting
  • Iteration, Filtering, and Boolean Indexing
  • Image Processing Using NumPy and Matplotlib

  • Data Structures in Pandas
  • Creating Data Frames and Loading Files
  • Data Exploration (EDA)

  • Seaborn Installation
  • Introduction to Seaborn
  • Basics of Plotting
  • Plots Generation
  • Visualizing the Distribution of a Dataset
  • Selection color palettes

Visualisation with Matplotlib

  • Matplotlib Installation
  • Matplotlib Basic Plots & it's
  • Containers
  • Matplotlib components and
  • properties
  • PyLab & Pyplot
  • Scatter plots
  • 2D Plots
  • Histograms
  • Bar Graphs
  • Pie Charts
  • Box Plots

This module aims to equip students with comprehensive knowledge and practical skills in Artificial Intelligence (AI). Students will explore key AI concepts, methodologies, and tools for developing intelligent systems. The curriculum includes essential algorithms, data preprocessing methods, and model evaluation strategies. Additionally, students will gain hands-on experience with popular programming languages and frameworks used in AI application and software development.

  • Introduction To AI
  • Why AI is Required
  • What is Neuron
  • Architecture of Artificial Neural Network
  • Neural Network Modules
  • Activation Functions
  • Optimization Function
  • Cost function
  • Dense Neural Network
  • Regularization
  • Gradient Descent

  • Simple ANN Model

Image Classification

  • Basic Intro to CNN
  • CNN (Convolution Neural Network)
  • CNN Architecture Building
  • Transfer Learning (VGG16 / VGG 19 / RESNET 50 / Inception V3)

NLP (Natural Language Processing)

  • Basic Intro to NLP
  • Simple NLTK (stemming, lemmatization, regex, stop words, corpus, unigram, bigram,trigram)
  • BAG for words (count vectorization)
  • TD-IDF-term frequency inverse document frequency
  • Word embedding:
      • . GloVe
      . Word2Vec
      . FastText
      . Keyed Vector
      . TextBlob

Machine Learning (ML) is a subset of Artificial Intelligence that focuses on enabling computers to learn from data and make predictions or decisions without explicit programming. It encompasses supervised, unsupervised, and reinforcement learning, with applications, spanning Healthcare, Finance, Retail, Manufacturing, Telecommunications, Agriculture, Energy, Transportation, Education, and Entertainment industries.

Key aspects include various algorithm types, feature engineering, model evaluation, overfitting, and ethical considerations. The field is dynamic, emphasizing continuous learning and innovation across multiple industries.

Supervised Learning

  • Linear Regression / Multi-Linear Regression
  • Logistic Regression
  • Decision Tree (CART)
  • Ensemble Learning
  • Random Forest
  • XGBoost
  • K-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Naive Bayes Classifier (NBC)
  • Grid Search CV and Random Search CV
  • Linear Discriminant Analysis (LDA)

Unsupervised

  • Hierarchical Clustering / Dendrograms
  • K-Means Clustering
  • DBSCAN
  • MINI BATCH K-Means

Metrics

  • MAE / MSE/ RMSE / R2 and Adjusted R 2
  • AUC ROC CURVE / Precision / Recall / F1 score / Confusion Metrics

Dimension Reduction Models

  • PCA
  • Kernal PCA

TIME SERIES ANALYSIS

  • ARIMA
  • FB PROPHET

Hyperparameter Tuning / Advanced ML Models

  • Over fitting and underfitting
  • Cross Validation
  • Log Loss
  • Elastic net
  • Lasso and Ridge Regression
  • SMOTE
  • SKLEARN Using Hyperparameter
  • Model Evaluation
  • Gradient Descent

  • Introduction to Git & Distributed
  • Version Control
  • Git Life Cycle
  • Create Clone & Commit Operations
  • Push & Update Operations
  • Stash, Move, Rename & Delete
  • Operations

  • Selecting & Retrieving Data with SQL
  • Filtering, Sorting and Calculating Data With SQL
  • Subqueries and joins in SQL
  • Modifying and Analyzing Data With SQL

Learners Outcome

G-Tec Jain Education Learners Outcome
  • Ability to collect, clean, preprocess, and analyze structured datasets
  • Strong proficiency in Python for data manipulation and modeling
  • Practical understanding of supervised and unsupervised ML algorithms
  • Capability to improve model accuracy using tuning and feature engineering
  • Expertise in data visualization and insight communication
  • Confidence to solve real-world data problems independently or in teams

Career Outcome

    After completing the course, learners can confidently pursue roles such as:

  • Data Analyst
  • Junior Data Scientist
  • Machine Learning Engineer
  • Data Scientist
  • Senior Data Scientist
  • Data Science Consultant
  • Machine Learning Researcher

    • The program aligns with India’s fast-growing Data Science & AI job market, offering strong salary and career growth potential.

G-Tec Jain Education Career Outcome

Certificate

    • NSDC: NSDC certification is widely recognized and respected by employers in various industries. It signifies the individual's skills, knowledge, and competence in a specific sector or job role. By obtaining NSDC certification, individuals can demonstrate their commitment to professional development, stand out in the job market, and enhance their career prospects within their chosen industry
    G-Tec Jain Education NSDC
    • GJK:The Data Science Certificate provided by the G-TEC JAIN Keerti is a prestigious recognition awarded to individuals who successfully complete their data science courses. This certificate serves as a confirmation of your expertise and competence in the field of data science
    G-Tec Jain Education GJK
    • JAIN:The Data Science Certificate issued by JAINX University is a prestigious acknowledgment presented to individuals upon the successful completion of their data science courses. This certificate serves as a validation of your proficiency and competence in the field of data science.
    G-Tec Jain Education JAIN

Course Overview

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TESTIMONIALS OF DATA Science with ML and AI COURSE

Frequently Asked Questions

  • This course is ideal for students, working professionals, and career switchers aiming to build a career in Data Science, AI, or Machine Learning.
  • Basic computer knowledge is required. Prior exposure to Excel or programming is helpful but not mandatory.
  • The program is highly practical, with hands-on coding, live projects, and real business case studies.
  • Python, Google Colab, NumPy, Pandas, Matplotlib, Seaborn, SQL, Scikit-learn, Git, ML & AI frameworks.
  • Yes. Learners receive an industry-recognized certificate from NSDC, G-TEC JAIN Keerti, and JAINX University.
  • Yes. The program includes career guidance, interview preparation, and job assistance support.


keerti Learner Support

Throughout the course, learners will have access to dedicated support from instructors and course mentors. They can ask questions, seek clarification, and receive guidance to enhance their learning experience.
Additionally, the course provides a collaborative learning environment where students can interact with peers, share insights, and learn from each other's experiences.

For more details connect with us:

G-Tec Jain Education Phone Number
Phone Number
G-Tec Jain Education Email
Email
[email protected]

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