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Machine Learning Course for Working Professionals

Name: Quality Thought Software Training Institute
Brand: Quality Thought
Rating: 4.5 (2330 reviews)

Python - AI - ML - DL - NLP - MLOps

Quality Thought‘s data science program is designed for software developers and working IT professionals who want to move into Data Scientist and Machine Learning Engineer roles. Build practical skills in Python, statistics, machine learning, deep learning, generative AI and real-world projects through a structured machine learning course for working professionals. Explore More Courses from Quality Thought to find additional career-focused training programs.

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Batch Details & Schedule

Choose a learning format and schedule that fits your lifestyle

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Session Time

Course Duration

Course Features & Highlights

Everything you need to build practical skills and prepare for a successful Data Science career

Live Projects

Work on real-world projects from day one with industry use cases

Expert Trainers

Learn from industry professionals with 10+ years of experience

100% Placement Support

Dedicated placement cell with assistance until you get hired

Industry Certification

Get certified and boost your resume with recognized credentials

LMS Access

Access to learning materials and recorded sessions

Mock Interviews

Weekly mock interviews to prepare you for real job scenarios

Resume Building

Professional resume preparation and LinkedIn profile optimization

Soft Skills Training

Communication, aptitude, and personality development sessions

Additional Benefits

● Pay After Placement Options Available

● Flexible Payment Plans

● 6-12 Months LMS Access

● Doubt Clearing Sessions

● 24/7 Learning Support

● Mega Job Drives

Choose Your Learning Path

Select the program that best fits your career goals and availability

Exclusive Training

⏱ 1.5-3 hrs/day

Best for: Beginners & learners

Training by Industry practitioners
Capstone projects
LMS access for 6 months
Daily doubt clarification sessions
Resume building session
LinkedIn & Github guidance
Interview questions preparation
1 mock interview
Placement referral support
Course completion certificate from Quality Thought
Course Fee: ₹35,000

POIP (Placements Oriented Intensive Program)

⏱ 6 hrs/day

Best for: Job seekers aiming for placements

LMS access for 1 year
Soft skills & aptitude training
Assignments & mock tests/interviews
Monthly placement screening tests
Mandatory Mega drive participation for placements
12 months placement assistance
Course Fee: ₹45,000
Pay after placement: 30,000 (after job confirmation)

Company Oriented Internship Program(COIP)

⏱ 6–8 Hours Daily

Best for: Learners seeking internship + placement assistance

6 months Internship at Ramana Soft (Client Projects)
Capstone projects/real client project implementation
1:1 mentor support for complex tasks
No mega-drive needed – companies interview you directly
Internship completion certificate (6 months – Ramana Soft or Client)
Course Fee: ₹1,00,000
Pay after placement: 30,000 (after job confirmation)

Not sure which program to choose? Contact our counselors for a free consultation. We’ll help you select the best program based on your background, goals, and availability.

Complete Course Curriculum

Comprehensive modules covering every aspect of AI Data Science

Introduction to Data Science

What is Data Science?
Why data science?
Impact of data science
Future of Data Science
Data Science Life Cycle
Introduction to Pre-Core Python
Introduction to Jupiter Notebook
Overview of Data Science Real Time IDEs
Introduction to Google-Collaborator-Notebook
Introduction to UNIX Operating System

Programming In Python For Data Environments

Core Python and Adv. Python
Python Basics
Python Introduction
Python Data Structure: Lists and Arrays
Python Conditions and Branching
Python Functions and Methods
Exceptions and Files
Python OOPs and Advanced Coding
PDBC and DB Communications
Practice Questions in Python and Reviews
Live Application implementation

Python For Advanced Data Science

NumPy for Data Science
Pandas for Data Science
Matplotlib for Data Science
Seaborn for Data Science
Live Application implementation

Data Visualization

Basic Plotting for Data Visualisation
Data Manipulation for Visualisation
1D Data Analysis: Histograms, Boxplots, and Violin Plots
Power-Bi
Introduction to Power-Bi
Data Extraction Process
Data Transformations
Data Modelling and DAX
Data Visualization with Analytics
Power-Bi, Q&A & Data Insights
Live Application3: Visualization of world GDP and carbon dioxide emission
Live Application4: Using Folium Library for Geographic Overlays

Data Analysis in Excel & SQL

Introduction to Excel
Functions, Formulas and Charts
Pivots and Lookups
Ranges and Tables
Data Cleaning: Text Functions, Dates and Times
Conditional Formatting
Sorting and Filtering
Subtotals with Ranges
Data Visualization in Excel
Advanced Excel with AI Features
SQL – Overview and SQL Process
SQL Commands-RDBMS Concepts
SQL – RDBMS Databases
What is Database?
What is DBMS and RDBMS?
Sub Languages in SQL
SQL – Syntax-Data Types-Operators
Create-Select-Delete-Drop-Inset
Where-AND and OR Conjunctive Operators
Like-Top-Limit or ROWNUM
Order By-Group By-Distinct Keyword
SQL – Constraints-Joins-SQL – Indexes
SQL-Alter-TRUNCATE
Properties of Transactions
Select … Where
Connectivity with Python

Maths For Data Science

Statistics

Basics of Statistics
Types of Statistics
Population & Sample
Central Tendencies
Percentiles & Dispersion
Statistics implementation with Python-I
Range, Sample variance and Standard Deviation
Correlation &Causation
Hypothesis Testing
Parametric and Non Parametric Tests

Probability

What is probability?
Importance of Probability in ML
Basics of Probability
Random Variables
Probability Distributions
Maximum Likelihood
Bayes Theorem
Information Theory
Cross Entropy
Information Gain

Linear Algebra

Scalar, Vector
Vector Addition
Vector Subtraction
Multiplying a vector by a Scalar
Dot Product of two Vectors
Cross Product of two Vectors
Scalar, Vector and Matrix
Different types of Matrix
Transpose of a Matrix
Matrix Addition, Subtraction
Eigen Values of Eigen Vectors

Calculus

What Is Calculus?
Limits and Differential Calculus
Limits and Continuity
Evaluating Limits
Function Derivatives
Continuous Functions
Derivatives of Powers and Polynomials
Introduction to Multivariate Calculus

Data Structures & Algorithms Tutorial in Python

What are data structures?
Big O notation – Data Structures & Algorithms | Measuring time complexity
Arrays in Python| Big O Analysis| Static Vs Dynamic Array
Linked List – Issues with Arrays | Double Linked List | Big O Analysis
Hash Table – Hash Map | Implementing in Python
Collision Handling In Hash Table| Implementing Chaining in Python
Stack – in Different Languages | Using List as a stack| Deque as Stack
Queue – in Different Languages | Using List as a Queue| Stock Price Examples
Tree (General Tree) -Tree and Data Structure | Implementing in Python
Binary Tree | BST | Binary Search Tree
Graph Introduction – Edge| Node
Binary Search – Linear | Binary
Bubble Sort | Quick Sort | Insertion Sort| Merge Sort| Shell Sort -Techniques
Recursion in Python
More Exercises on DSA

Machine Learning

What is Machine Learning?
Types of Machine Learning: Supervised Learning, Unsupervised Learning
Applications of Machine Learning
Types of Data: Continuous and Categorical
Data Exploration and Visualization
- - Descriptive Statistics
  - Inferential Statistics
  - Data Distributions
  - Correlation and Covariance
  - Handling Missing Values
  - Data Visualizations Scatter Plots and Heatmaps
Data Normalization Techniques
Data Imputation Techniques

Regression

Introduction to Regression
Simple Linear Regression
Multiple Linear Regression
Linear Regression Assumptions
Regularization Techniques (Lasso Regression, Ridge Regression)
Polynomial Regression
Stepwise Regression
ElasticNet Regression
R-Squared and Adjusted R-Squared

Classification

Introduction to Classification
Types of Classifiers
Linear Classifiers (Logistic Regression, Multinomial Logistic Regression)
Non-Linear Classifiers
Decision Trees (CART Algorithm, ID3 Algorithm)
Random Forests
Support Vector Machines (SVMs) (Kernel Trick, Soft Margin SVMs, Multi-Class SVMs)
K-Nearest Neighbors (KNN)
Naive Bayes
Neural Networks for Classification (Perceptron Algorithm, Multilayer Perceptron (MLP), Backpropagation Algorithm, Activation Functions)
Evaluation Metrics for Classification (Confusion Matrix, Accuracy, Precision, Recall, F1-Score, ROC Curve, AUC)
Features & Model Selection (Feature Selection Techniques, Hyperparameter Tuning Techniques, Model Selection Techniques – Bias-Variance Tradeoff, Cross-Validation, Leave One Out Cross Validation)
Ensemble Learning (Ensemble Methods, Bagging Algorithms, Boosting Algorithms – XGBoost Algorithm, Gradient Boosting Algorithm, LightGBM Algorithm, CatBoost Algorithm, Adaboost Algorithm, Stacking Technique, Blending Technique)

Clustering

Introduction to Clustering
K-Means Clustering
Hierarchical Clustering

Dimensionality Reduction

Introduction to Dimensionality Reduction
Principal Component Analysis (PCA)
Sigular Value Decomposition (SVD)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Linear Discriminant Analysis (LDA)
Truncated SVD

Deep Learning

What is Deep Learning
Different Between Machine Learning and Deep Learning
What is Biological Neural Network
What is Deep Learning Application
What is Artificial Neural Network (ANN)
What is Convolutional Neural Network (CNN)
What is Recurrent Neural Network (RNN)
CNN & Computer Vision
Intro to Images and Image Pre-processing with OpenCV CNN Architecture
Image Classification Case Study
Case Study with Transfer Learning

Natural Language Processing

Introduction to text and Text Pre-processing with nltk and spacy
Vectorization Techniques
Project – Text Classification
RNNs
Project – Sequence Tagging
LSTMs
Auto Encoders

Add on Content For Internship Students

Add on Content For Internship Students
Prompt Engineering
Prompting Techniques for Generative Models
LLMs for Word Embedding and Chunking Mechanism
CRT, VERBAL & SOFT SKILLS
1. Aptitude
2. Reasoning
3. Verbal(English)
Soft Skills Based On LSRW Rule
1. Grammar sessions
2. Communication Skills
Interview Skills
Professional Skills and Additional

Note: Common Syllabus for Cocubes, Elimus, AMCAT & TCS NQT & for all other MNC companies

Multiple Comprehensive Program Tracks

Intensive / Internship Training with 20+ Realtime Projects

Prerequisites & Eligibility

Everything you need to build practical Data Science and AI skills

Willingness to engage in Aptitude, Soft Skills & Interview Readiness sessions
Commitment to complete Full Day Training or Intensive/Internship program schedules

Any Graduate eligible for enrollment across all program tracks
Dedication to work on Realtime Scenario Projects and live client project implementations

Special Note: This program is suitable for professionals planning a career change to data science, developers moving from data analyst to data scientist roles, and learners looking for data science training in Hyderabad with practical, career-focused training.

Why Choose Quality Thought

Your success is our priority. Here’s what makes us the best choice for your career growth

17+

Years Experience

10,000+

Students Trained

500+

Hiring Partners

95%

Placement Rate

17+ Years of Excellence

Established training institute with proven track record of producing industry-ready professionals

Expert Faculty

Learn from trainers with 10+ years of real-world industry experience in leading tech companies

100% Placement Assistance

Dedicated placement cell with tie-ups with 500+ companies. We support you until you get hired

Comprehensive Curriculum

Updated syllabus covering latest technologies and industry best practices with hands-on projects

Career Growth Focus

Not just training, but complete career transformation with soft skills and interview preparation

Pay After Placement

Flexible payment options including pay after placement for eligible candidates

Flexible Batches

Multiple batch timings to suit working professionals, students, and freshers

Live Project Experience

Work on real client projects during internship at Ramana Soft IT company

Quality Thought provides practical learning and career support for individuals and organizations, including customized data science corporate training and structured practical experience through a data science internship course.

Certification

Get industry-recognized certification that validates your skills and boosts your career prospects

Certificate of Completion

Data science with AIML

This certifies that

[Your Name]

has successfully completed the

Data science with AIML

Training Program

Quality Thought

Ameerpet, Hyderabad

Certificate ID

QT-2024-XXXX

Industry-recognized course completion certificate
Certification from Quality Thought institute
Internship Completion Certificate for Eligible Internship Programs
Digital certificate with unique verification ID
Shareable on LinkedIn and other professional platforms
Valid proof of skill acquisition for employers
Includes detailed syllabus covered
Project completion certificates

Industry Recognition

Our certificates are recognized by leading companies and add credibility to your resume

Skill Validation

Proves your expertise in Data Science with AI in all mentioned technologies

Career Advancement

Increases your chances of getting hired and helps in salary negotiations

Digital & Physical

Get both digital certificate for online sharing and physical certificate for framing

Course Certificate

Upon successful completion of training program

Internship Certificate

For I&I program from Ramana Soft IT Company

Project Certificate

For major projects completed during training

Frequently Asked Questions

Find answers to common questions about our Data science with AI training program

I am a software developer. What will actually be new for me?

For working developers, the new material is statistics and experiment design, model evaluation discipline, and the machine learning and deep learning modules. Python, Git, APIs and deployment experience transfer directly and shorten the learning curve. These skills can also support learners targeting machine learning engineer jobs USA.

Is there a weekend or evening batch for working professionals?

Please confirm current weekday-evening and weekend batch timings before publishing. Quality Thought can provide batch options designed to help working professionals balance structured learning with their existing work schedules. Learners can also compare these options when evaluating the best data science course 2026.

What does the deep learning module cover?

It covers neural network fundamentals and backpropagation, activation and loss functions, optimisers and learning rates, regularisation, convolutional networks for images, sequence models and transfer learning with pretrained models. These concepts provide a foundation for a data science with AI and machine learning course.

How much NLP is included?

The NLP module covers text processing, bag-of-words and TF-IDF, word embeddings, named entity recognition, text classification and sentiment analysis, then connects these concepts to transformers and LLMs in the generative AI module. Learners also gain practical exposure through a data science real time project.

Does the course cover MLOps properly?

Yes, MLOps covers experiment tracking, model and data versioning, packaging a model behind an API, deployment, monitoring and retraining on drift, using Git and GitHub throughout.

Should I aim for Data Scientist or ML Engineer roles?

Aim for the role that best matches your existing systems and production ownership, while also considering your interest in analytics and experimentation. The program builds a practical foundation that can support professionals pursuing a data science job guarantee course as part of their career planning.

Do I need to learn statistics if I am already a strong programmer?

Yes, and it is usually the gap that costs developers offers. Interviews for these roles probe experiment design, significance and validation more often than they probe coding ability. A structured data science course for freshers can introduce these foundations, although this program is also suitable for professionals with programming experience.

Can I skip the Python and Excel foundation modules?

The foundation modules help establish the common workflow used throughout the program. Learners with prior experience may find some topics familiar, but understanding the full workflow helps connect programming, analytics and machine learning skills. The practical approach is also useful for learners considering a data science bootcamp course.

Do you cover GPU training and where to run deep learning workloads?

Yes, the course covers using Google Colab for GPU-backed training and the practical trade-offs of local versus hosted compute. Confirm whether any dedicated GPU access is provided.

Is generative AI covered deeply enough to work with LLMs professionally?

The generative AI module covers prompt engineering, LLMs and RAG applied to data problems. Learners work with practical production-oriented concepts so they can understand how generative AI can be applied in professional environments. The program can also complement a data science certification pathway for professionals building their credentials.

Ready To Become an AI & Data Science Professional?

Don’t wait! Join thousands of successful students who transformed their careers with Quality Thought. Your journey to mastering Python, AI, Machine Learning, Deep Learning, NLP, and MLOps – and landing roles as a Data Scientist, AI Engineer, or ML Engineer – starts here.