Professional AI & Machine Learning Program: Foundations to Industry Projects

Build a foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML)-its core concepts, algorithms, model types, and real-world applications for businesses and individuals.

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Payment Schedule of Professional AI & ML Course

50% of the course fee is due at the time of registration.
25% is payable after 60 days from the start of your classes.
The remaining 25% is due after 120 days from the start of your classes.
This allows you to split your payments while continuing your course without interruption.

AI & ML Course Highlights

Makes AI and ML easy to understand for non-technical learners.
Helps you explore career paths in AI engineering, ML engineering, data science, and analytics.
Improves your analytical skills and job readiness for IT and business roles alike.

About The AI & ML Online Course

Entry-level program designed for beginners with no prior AI or ML experience.
Core coverage: AI and ML fundamentals, supervised and unsupervised learning, neural networks, and deep learning.
Tools overview: Introduction to Python, TensorFlow and popular ML libraries.

Starting date - Saturday, September 13, 2025
Training Duration : 6 months (144 hrs).

Duration : 3 hours.
Class Mode : Online.

Schedule : Weekend.
Saturday & Sunday : 10 AM to 1 PM.

Course Overview and Curriculum Outline

Month 1: Foundations of AI & ML (Weeks 1–5)

Week 1 : Introduction to AI & ML, Understanding AI Problem-Solving

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Saturday: Exploratory Data Analysis (EDA) – Concepts
What is EDA and Why It Matters	0–30 min
Understanding Data Distributions	30–60 min
Statistical Summaries and Insights	60–90 min
Correlation and Covariance	90–120 min
Identifying Patterns and Trends	120–150 min
Recap + Q&A	150–180 min

Sunday: Visual EDA using Python Libraries
Introduction to Matplotlib	0–30 min
Seaborn for Statistical Visualization	30–60 min
Creating Boxplots, Pairplots, and Heatmaps	60–90 min
Customizing Visual Styles	90–120 min
Hands-on: EDA on Real Dataset	120–150 min
Recap + Q&A	150–180 min

Week 2 : Python Mastery

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Saturday: Python Mastery – Part 1
Python Basics – Variables, Data Types, Loops	0–30 min
Functions and Conditional Statements	30–60 min
Working with Libraries (NumPy, Pandas)	60–90 min
Data Manipulation in Pandas	90–120 min
Visualizing Data with Matplotlib	120–150 min
Recap + Q&A	150–180 min

Sunday: Python Mastery – Part 2
Advanced Visualization using Seaborn	0–30 min
File Handling and Exception Management	30–60 min
Introduction to SQL for Data Handling	60–90 min
Working with DataFrames and CSV Files	90–120 min
Mini Hands-on: Exploratory Data Manipulation	120–150 min
Recap + Q&A	150–180 min

Week 3 : Mathematical Foundations

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Saturday: Mathematical Foundations – Part 1
Basics of Linear Algebra for ML	0–30 min
Vectors and Matrices Operations	30–60 min
Eigenvalues and Eigenvectors in ML	60–90 min
Probability and Statistics Fundamentals	90–120 min
Distributions and Sampling	120–150 min
Recap + Q&A	150–180 min

Sunday: Mathematical Foundations – Part 2
Calculus and Optimization Concepts	0–30 min
Gradient and Derivative Understanding	30–60 min
Cost Function and Gradient Descent	60–90 min
Statistical Measures for Model Evaluation	90–120 min
Hands-on: Implement Basic Math in Python	120–150 min
Recap + Q&A	150–180 min

Week 4 : Review, Hands-on, Mock Test & Interview Prep

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Saturday: Review, Hands-on, Mock Test & Interview Prep
Recap of Month 1 Concepts	0–60 min
Hands-on: Python Data Manipulation & Math Exercises	60–120 min
Practice Quiz: AI, Python, and Math Fundamentals	120–180 min

Sunday: Review, Hands-on, Mock Test & Interview Prep
Mock Test (20 Questions)	0–60 min
Interview Q&A: “What is AI & ML?”	60–120 min
Feedback & Open Q&A	120–180 min

Week 5 : Introduction to Data Preparation, Data Preprocessing Techniques

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Saturday: Introduction to Data Preparation
What is Data Preparation?	0–30 min
Importance of Data Cleaning	30–60 min
Handling Missing and Duplicate Data	60–90 min
Removing Outliers and Noise	90–120 min
Data Quality and Integrity Checks	120–150 min
Recap + Q&A	150–180 min

Sunday: Data Preprocessing Techniques
Data Transformation and Normalization	0–30 min
Encoding Categorical Variables	30–60 min
Feature Scaling (MinMax, Standardization)	60–90 min
Data Splitting – Train/Test	90–120 min
Hands-on: Data Cleaning in Python (Pandas)	120–150 min
Recap + Q&A	150–180 min

Month 2: (Weeks 6–8)

Week 6 : Exploratory Data Analysis (EDA) – Concepts, Visual EDA using Python Libraries

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Saturday: Exploratory Data Analysis (EDA) – Concepts
What is EDA and Why It Matters	0–30 min
Understanding Data Distributions	30–60 min
Statistical Summaries and Insights	60–90 min
Correlation and Covariance	90–120 min
Identifying Patterns and Trends	120–150 min
Recap + Q&A	150–180 min

Sunday: Visual EDA using Python Libraries
Introduction to Matplotlib	0–30 min
Seaborn for Statistical Visualization	30–60 min
Creating Boxplots, Pairplots, and Heatmaps	60–90 min
Customizing Visual Styles	90–120 min
Hands-on: EDA on Real Dataset	120–150 min
Recap + Q&A	150–180 min

Week 7 : Feature Engineering – Transforming Data, Dimensionality Reduction Techniques

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Saturday: Feature Engineering – Transforming Data
What is Feature Engineering?	0–30 min
Creating New Features from Existing Data	30–60 min
Feature Encoding (One-Hot, Label Encoding)	60–90 min
Feature Selection Techniques	90–120 min
Handling Imbalanced Data	120–150 min
Recap + Q&A	150–180 min

Sunday: Dimensionality Reduction Techniques
Introduction to Dimensionality Reduction	0–30 min
Principal Component Analysis (PCA)	30–60 min
Interpreting PCA Components	60–90 min
Hands-on: PCA in Python	90–120 min
t-SNE and Feature Compression Overview	120–150 min
Recap + Q&A	150–180 min

Week 8 : Business Intelligence (BI) and Dashboarding, Review, Hands-on, Mock Test & Interview Prep

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Saturday: Business Intelligence (BI) and Dashboarding
What is Business Intelligence?	0–30 min
Importance of Visual Analytics	30–60 min
Overview of Tableau and Power BI	60–90 min
Hands-on: Build Dashboard in Tableau	90–120 min
Power BI Visualization and Comparison	120–150 min
Power BI Visualization and Comparison	120–150 min
Recap + Q&A	150–180 min

Sunday: Review, Hands-on, Mock Test & Interview Prep
Recap of Data Preparation and Visualization Concepts	0–60 min
Hands-on: Data Analysis Project using Python + Tableau	60–120 min
Mock Test + Interview Q&A: “How do you clean and visualize data?”	120–180 min

Month 3: Core Machine Learning (Weeks 9–12)

Week 9 : Introduction to Machine Learning Concepts, Supervised Learning – Fundamentals

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Saturday: Introduction to Machine Learning Concepts
What is Machine Learning?	0–30 min
ML Workflow – Data to Model	30–60 min
Types of Learning – Supervised, Unsupervised, Reinforcement	60–90 min
Key Components – Features, Labels, Algorithms	90–120 min
Use Cases of ML in Real-world Scenarios	120–150 min
Recap + Q&A	150–180 min

Sunday: Supervised Learning – Fundamentals
What is Supervised Learning?	0–30 min
Regression vs Classification	30–60 min
Linear Regression – Concept & Implementation	60–90 min
Logistic Regression – Binary Classification	90–120 min
Hands-on: Implement Linear & Logistic Regression	120–150 min
Recap + Q&A	150–180 min

Week 10 : Supervised Learning – Advanced Models, Unsupervised Learning – Clustering Techniques

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Saturday: Supervised Learning – Advanced Models
Decision Trees – Concept & Intuition	0–30 min
Overfitting and Pruning	30–60 min
Random Forest Algorithm	60–90 min
Gradient Boosting and XGBoost	90–120 min
Hands-on: Build Classification Model	120–150 min
Recap + Q&A	150–180 min

Sunday: Unsupervised Learning – Clustering Techniques
What is Unsupervised Learning?	0–30 min
K-Means Clustering – Intuition	30–60 min
Hierarchical Clustering	60–90 min
Dimensionality Reduction – PCA Overview	90–120 min
Hands-on: Apply Clustering on Real Dataset	120–150 min
Recap + Q&A	150–180 min

Week 11 : Ensemble Methods – Boosting Model Performance, Model Evaluation Techniques

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Saturday: Ensemble Methods – Boosting Model Performance
Introduction to Ensemble Learning	0–30 min
Bagging and Random Forests	30–60 min
Boosting – AdaBoost, Gradient Boost	60–90 min
XGBoost Implementation	90–120 min
Hands-on: Compare Ensemble Models	120–150 min
Recap + Q&A	150–180 min

Sunday: Model Evaluation Techniques
Evaluation Metrics – Accuracy, Precision, Recall, F1	0–30 min
Confusion Matrix Interpretation	30–60 min
ROC-AUC and Cross-Validation	60–90 min
Bias-Variance Tradeoff	90–120 min
Hands-on: Evaluate Model on Real Dataset	120–150 min
Recap + Q&A	150–180 min

Week 12 : End-to-End ML Pipeline Development, Review, Hands-on, Mock Test & Interview Prep

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Saturday: End-to-End ML Pipeline Development
What is an ML Pipeline?	0–30 min
Data Preprocessing and Feature Engineering	30–60 min
Model Building and Tuning	60–90 min
Model Validation and Testing	90–120 min
Hands-on: Build Complete ML Pipeline	120–150 min
Recap + Q&A	150–180 min

Sunday: Review, Hands-on, Mock Test & Interview Prep
Recap of ML Concepts (Supervised, Unsupervised, Ensemble)	0–60 min
Hands-on: Mini ML Project	60–120 min
Mock Test + Interview Q&A: “How do you choose the right ML model?”	120–180 min

Month 4: Advanced Machine Learning & Deep Learning (Weeks 13–16)

Week 13 : Recommender Systems – Fundamentals, Advanced Recommender Systems

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Saturday: Recommender Systems – Fundamentals
What are Recommender Systems?	0–30 min
Collaborative vs Content-Based Filtering	30–60 min
Understanding Similarity Metrics (Cosine, Pearson)	60–90 min
Matrix Factorization Techniques	90–120 min
Hands-on: Build Simple Recommender in Python	120–150 min
Recap + Q&A	150–180 min

Sunday: Advanced Recommender Systems
Hybrid Recommendation Systems	0–30 min
Implicit vs Explicit Feedback	30–60 min
Evaluation Metrics (RMSE, MAE, Precision@K)	60–90 min
Collaborative Filtering using Surprise Library	90–120 min
Hands-on: Movie Recommendation System	120–150 min
Recap + Q&A	150–180 min

Week 14 : Advanced Optimization in Machine Learning, Large-Scale Machine Learning & MLOps Basics

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Saturday: Advanced Optimization in Machine Learning
Understanding Optimization in ML	0–30 min
Gradient Descent Variants (SGD, Mini-batch, Adam)	30–60 min
Learning Rate Scheduling and Regularization	60–90 min
Hyperparameter Tuning (Grid & Random Search)	90–120 min
Hands-on: Optimize Model with GridSearchCV	120–150 min
Recap + Q&A	150–180 min

Sunday: Large-Scale Machine Learning & MLOps Basics
Distributed Training and Parallel Processing	0–30 min
Introduction to MLOps and Workflow Automation	30–60 min
Model Tracking and Versioning	60–90 min
Deployment using Flask / FastAPI	90–120 min
Monitoring & Model Drift Concepts	120–150 min
Recap + Q&A	150–180 min

Week 15 : Neural Network Fundamentals, Convolutional Neural Networks (CNNs)

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Saturday: Neural Network Fundamentals
Introduction to Neural Networks	0–30 min
Perceptrons and Activation Functions	30–60 min
Multi-Layer Perceptrons (MLPs)	60–90 min
Backpropagation and Gradient Descent	90–120 min
Hands-on: Build Basic NN using TensorFlow	120–150 min
Recap + Q&A	150–180 min

Sunday: Convolutional Neural Networks (CNNs)
What are CNNs?	0–30 min
Convolution, Pooling, and Filters	30–60 min
Building CNN for Image Classification	60–90 min
Transfer Learning using Pre-trained Models	90–120 min
Hands-on: CNN on CIFAR-10 Dataset	120–150 min
Recap + Q&A	150–180 min

Week 16 : Recurrent Neural Networks (RNNs) and LSTMs, Transformers and Model Evaluation

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Saturday: Recurrent Neural Networks (RNNs) and LSTMs
What are Recurrent Networks?	0–30 min
RNN Architecture and Working	30–60 min
LSTMs and GRUs for Sequential Data	60–90 min
Text Prediction and Sentiment Analysis	90–120 min
Hands-on: Build RNN in TensorFlow	120–150 min
Recap + Q&A	150–180 min

Sunday: Transformers and Model Evaluation
Introduction to Attention Mechanisms	0–30 min
Transformers in Deep Learning	30–60 min
Using BERT and GPT Models	60–90 min
Hands-on: Text Classification using Transformers	90–120 min
Review: Neural Networks and Optimization	120–150 min
Mock Test + Interview Q&A	150–180 min

Month 5: Applied AI Domains & Industry Projects (Weeks 17–20)

Week 17 : Natural Language Processing (NLP)

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Saturday: Natural Language Processing (NLP) – Fundamentals
Introduction to NLP and its Real-world Applications	0–30 min
Text Preprocessing – Tokenization, Lemmatization	30–60 min
Stopwords Removal and Text Normalization	60–90 min
Bag of Words and TF-IDF Vectorization	90–120 min
Hands-on: Text Cleaning using NLTK	120–150 min
Recap + Q&A	150–180 min

Sunday: NLP – Advanced Concepts
Introduction to Word Embeddings (Word2Vec, GloVe)	0–30 min
Named Entity Recognition using SpaCy	30–60 min
Transformer Models – BERT Overview	60–90 min
Text Classification using Transformers	90–120 min
Hands-on: Sentiment Analysis Project	120–150 min
Recap + Q&A	150–180 min

Week 18 : Computer Vision – Image Processing Basics, Advanced Computer Vision Applications

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Saturday: Computer Vision – Image Processing Basics
What is Computer Vision?	0–30 min
Image Representation and Filters	30–60 min
Edge Detection and Contours	60–90 min
Convolution and Feature Maps	90–120 min
Hands-on: Build Basic Image Classifier using CNN	120–150 min
Recap + Q&A	150–180 min

Sunday: Advanced Computer Vision Applications
Object Detection using OpenCV	0–30 min
Image Segmentation and Region Proposals	30–60 min
Transfer Learning using Pre-trained Models (ResNet, VGG)	60–90 min
Deploying Vision Models for Real-time Detection	90–120 min
Hands-on: Image Classification Project	120–150 min
Recap + Q&A	150–180 min

Week 19 : Time Series & Reinforcement Learning – Fundamentals, Reinforcement Learning & Ethical AI

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Saturday: Time Series & Reinforcement Learning – Fundamentals
Introduction to Time Series and Sequential Data	0–30 min
ARIMA and Forecasting Models	30–60 min
Feature Engineering for Temporal Data	60–90 min
Anomaly Detection in Time Series	90–120 min
Hands-on: Stock Price Prediction using Python	120–150 min
Recap + Q&A	150–180 min

Sunday: Reinforcement Learning & Ethical AI
Introduction to Reinforcement Learning	0–30 min
Key Concepts – Agent, Environment, Reward	30–60 min
Q-Learning and Policy Gradients	60–90 min
Bias and Fairness in AI Models	90–120 min
Hands-on: Ethical AI Case Study Discussion	120–150 min
Recap + Q&A	150–180 min

Week 20 : Industry Project 1 – Movie Recommendation System, Industry Project 2 – Fashion Discovery Engine & Healthcare AI

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Saturday: Industry Project 1 – Movie Recommendation System
Overview of Collaborative Filtering	0–30 min
Building Similarity Matrix	30–60 min
Matrix Factorization using Python	60–90 min
Predicting Movie Ratings	90–120 min
Hands-on: Build Netflix-style Recommender	120–150 min
Recap + Q&A	150–180 min

Sunday: Industry Project 2 – Fashion Discovery Engine & Healthcare AI
Product Recommendation using Computer Vision	0–30 min
Implementing Image-based Search (Fashion Engine)	30–60 min
Healthcare ML Model – Cancer Prediction	60–90 min
Deploy Models in Real-time Dashboard	90–120 min
Project Review & Peer Discussion	120–150 min
Recap + Feedback	150–180 min

Month 6: Career Readiness & Launch (Weeks 21–24)

Week 21 : Portfolio Development – Resume & GitHub Setup, Portfolio Enhancement & Presentation Skills

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Saturday: Portfolio Development – Resume & GitHub Setup
Introduction to Professional Branding	0–30 min
Resume Writing for AI & Data Roles	30–60 min
Building Project Highlights & Achievements	60–90 min
Creating GitHub Repository for Projects	90–120 min
Hands-on: Upload and Document Capstone Project	120–150 min
Recap + Q&A	150–180 min

Sunday: Portfolio Enhancement & Presentation Skills
Enhancing GitHub Readme with Visuals & Demos	0–30 min
Structuring a Project Portfolio Website	30–60 min
LinkedIn Optimization for AI Professionals	60–90 min
Building an AI-Focused Career Profile	90–120 min
Hands-on: Portfolio Review and Peer Feedback	120–150 min
Recap + Q&A	150–180 min

Week 22 : Cloud & MLOps – Deployment Basics, Advanced MLOps – Production Pipeline Setup

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Saturday: Cloud & MLOps – Deployment Basics
Introduction to MLOps Concepts	0–30 min
Model Deployment Overview – AWS, GCP, Azure	30–60 min
Using Docker for ML Model Packaging	60–90 min
Deploying Models via Flask / FastAPI	90–120 min
Hands-on: Deploy Model to Cloud	120–150 min
Recap + Q&A	150–180 min

Sunday: Advanced MLOps – Production Pipeline Setup
CI/CD for Machine Learning	0–30 min
Model Monitoring and Drift Detection	30–60 min
Version Control for Models (DVC, MLflow)	60–90 min
Automated Retraining and Updates	90–120 min
Hands-on: Build MLOps Workflow on Cloud	120–150 min
Recap + Q&A	150–180 min

Week 23 : Interview Preparation – Technical, Behavioral & HR Rounds

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Saturday: Interview Preparation – Technical Round
Common ML and AI Interview Questions	0–30 min
Problem-Solving in Python and SQL	30–60 min
ML Case Studies and Scenario-based Q&A	60–90 min
Mock Coding Interview	90–120 min
Hands-on: Solve Real-World ML Problems	120–150 min
Recap + Q&A	150–180 min

Sunday: Interview Preparation – Behavioral & HR Rounds
How to Present Your AI Projects	0–30 min
STAR Method for Behavioral Answers	30–60 min
Communicating Complex AI Concepts Simply	60–90 min
Common HR Interview Mistakes to Avoid	90–120 min
Mock Interview Practice	120–150 min
Feedback & Review	150–180 min

Week 24 : Future of AI – Industry Insights & Trends, Final Review, Mock Exam & Career Launch Plan

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Saturday: Future of AI – Industry Insights & Trends
Introduction to GenAI and Edge AI	0–30 min
Emerging Use Cases in 2025 and Beyond	30–60 min
Responsible AI and Governance Models	60–90 min
Ethical Deployment & AI Risk Management	90–120 min
Hands-on: Discussion – “AI for Good”	120–150 min
Recap + Q&A	150–180 min

Sunday: Final Review, Mock Exam & Career Launch Plan
Full Program Review	0–30 min
Mock Test – AI, ML, and MLOps	30–60 min
Interview Simulation – Technical + HR	60–90 min
Final Project Presentation	90–120 min
Career Planning and Next Steps	120–150 min
Feedback & Closing Ceremony	150–180 min

The world is rapidly embracing Artificial Intelligence and Machine Learning. From the apps you use daily to complex enterprise systems, AI and ML are reshaping how technology is built, deployed, and leveraged for decision-making. For aspiring IT professionals and graduates, understanding AI and ML is no longer optional—it’s a necessity. At Viewsoft Academy, we have designed a comprehensive Professional AI & ML Program to give you the competence and knowledge to thrive in this transformative field.

It’s more than just another course; it is your gateway to a rewarding career with our AI & ML program. We’ve maintained simplicity while delivering a practical, hands-on curriculum that covers the core concepts of AI, ML, and deep learning. Whether you’re a recent graduate seeking a competitive edge or an IT professional aiming to reskill or upskill, our program equips you for success in the world of AI and ML.

Why AI & ML is the Future?

The words Artificial Intelligence and Machine Learning are no longer just buzzwords—they are now foundational across industries worldwide. Organizations of all sizes are leveraging AI and ML to gain insights, improve efficiency, and drive innovation. This widespread adoption has created a huge demand for skilled professionals capable of designing, developing, and deploying AI and ML solutions.

One of the most effective ways to demonstrate your expertise and stand out to employers is by earning an AI & ML certification. It validates your ability to tackle real-world challenges and showcases your practical understanding. Our program prepares you for highly sought-after industry certifications, such as the TensorFlow Developer Certificate and AWS Machine Learning Specialty, providing you with the foundational knowledge to pursue these credentials confidently.

Why is the Viewsoft Academy Program the Right One?

At Viewsoft Academy, we follow a philosophy of practical, hands-on learning. Our AI & ML curriculum is designed and delivered by industry professionals who have led major AI and ML projects in real-world environments. We don’t just teach theory—we demonstrate concepts and give you the opportunity to apply them directly.

Multifaceted Curriculum: Our Professional AI & ML Program covers all essentials, including supervised and unsupervised learning, neural networks, deep learning, natural language processing, computer vision, and model deployment, along with practical coding in Python and popular ML libraries.

Relevant Industry Content: We regularly update our content to reflect the latest developments in AI and ML, ensuring that your skills are current and immediately applicable in today’s workplace.

Skilled Instructors: Learn from experienced AI & ML professionals who bring real-life examples and case studies to the classroom, making the learning experience engaging, practical, and relevant.

Flexible Learning: Our program is designed to fit your schedule. With online access to lectures, hands-on labs, and assignments, you can learn at your own pace, anytime and anywhere, without compromising your professional or personal commitments.

Who is to take part in this Program?

This program is perfect for:

IT Professionals who want to transition into AI & ML roles or implement intelligent solutions within their organizations. The Viewsoft Academy Certificate in AI & ML validates your expertise and expands your career opportunities in this rapidly growing field.
Computer Science, IT, and related graduates who wish to enhance their resumes with a recognized AI & ML certification, making them more attractive to recruiters in technology-driven industries.
Beginners and career changers looking to build a strong foundation in AI & ML and launch a successful career in one of today’s most in-demand and impactful technology domains.

The Ultimate Guide to Highest Certifications and your future.

Although this course covers the fundamentals of AI & ML, it also serves as a strong stepping stone toward more advanced and specialized areas in artificial intelligence and machine learning. The concepts and skills you gain are directly applicable to major industry certifications. For example, our program prepares you for credentials such as the TensorFlow Developer Certificate and the AWS Machine Learning Specialty.

As AI & ML technologies continue to evolve, additional certifications—like Google Cloud AI & ML or Databricks Machine Learning—are becoming increasingly recognized. Our curriculum provides a robust, vendor-neutral foundation, ensuring you are well-prepared to pursue any of these certifications. Completing the program gives you the clarity, knowledge, and confidence to select and achieve the certifications that best align with your career goals.

Why This is the Opportunity You can not Afford to Miss.

AI and ML professional roles are growing at an unprecedented rate. Delaying entry into this field means missing out on high-paying opportunities and rapid career advancement. Our Professional AI & ML Program is designed to make you market-ready quickly and efficiently. By mastering practical skills, algorithms, and model-building techniques, you will be equipped to tackle real-world AI challenges and make a meaningful impact in any organization.

Become one of the thousands of professionals who have relied on Viewsoft Academy to improve their careers. We are devoted to high-quality education and the academic success of our students.

Frequently Asked Questions on The Professional AI & ML Course By Viewsoft Academy

Question 1. Is there a prerequisite to taking the AI & ML Course?

Answer: The course is beginner-friendly and does not require any prior AI or ML experience. A basic understanding of IT concepts is helpful, but the curriculum starts from the fundamentals. It is best suited for graduate students, recent graduates, or IT professionals who want to acquire a new skill set in artificial intelligence and machine learning.

Question 2. How much time does it take to go through the program?

Answer: The program can be self-paced, and thus, you can learn at your own pace. Students, on average, finish the course in 8-12 weeks, spending between 5-7 hours per week on video lectures and practical work in the labs. The course material is accessible to you throughout life.

Question 3. What type of certificate will I get at the end?

Answer: After successfully completing all modules and assessments, you will be eligible to receive an official AI & ML certificate from Viewsoft Academy. This professional certificate validates your fundamental understanding of AI and ML concepts and is a valuable credential to showcase on your resume and professional profiles.

Question 4. Does the course assure employment or certification?

Answer: Our program does not guarantee employment, but it equips you with the essential skills and foundational knowledge required for entry-level roles in AI and ML. It also serves as a stepping stone toward formal industry certifications, such as the TensorFlow Developer Certificate and AWS Machine Learning Specialty, which are highly valued in today’s job market.

Question 5. What is the format of the practical learning?

Answer: The course includes numerous practical exercises and hands-on labs that allow you to work directly with AI and ML technologies. You will apply theory to practice, developing skills in data preprocessing, model building, algorithm implementation, and deployment, ensuring you are fully prepared for real-world projects and technical interviews.

Question 6. Who are the instructors? What are their qualifications?

Answer: Our trainers are seasoned AI and ML professionals with extensive experience designing, implementing, and managing AI/ML solutions in large organizations. They bring real-world insights into the curriculum, providing practical perspectives that go beyond theory and prepare you to tackle challenges in today’s data-driven and AI-powered industry.

Professional AI & Machine Learning Program: Foundations to Industry Projects

Payment Schedule of Professional AI & ML Course

AI & ML Course Highlights

About The AI & ML Online Course

Course Overview and Curriculum Outline

Month 1: Foundations of AI & ML (Weeks 1–5)

Month 2: (Weeks 6–8)

Month 3: Core Machine Learning (Weeks 9–12)

Month 4: Advanced Machine Learning & Deep Learning (Weeks 13–16)

Month 5: Applied AI Domains & Industry Projects (Weeks 17–20)

Month 6: Career Readiness & Launch (Weeks 21–24)

Why AI & ML is the Future?

Why is the Viewsoft Academy Program the Right One?

Who is to take part in this Program?

The Ultimate Guide to Highest Certifications and your future.

Why This is the Opportunity You can not Afford to Miss.

Frequently Asked Questions on The Professional AI & ML Course By Viewsoft Academy

We don’t send spam so don’t worry.

Capability Statement

Get In Touch

Professional AI & Machine Learning Program: Foundations to Industry Projects

Fill Out the Form Below to Get All the Details!

Payment Schedule of Professional AI & ML Course

AI & ML Course Highlights

About The AI & ML Online Course

Course Overview and Curriculum Outline

Month 1: Foundations of AI & ML (Weeks 1–5)

Month 2: (Weeks 6–8)

Month 3: Core Machine Learning (Weeks 9–12)

Month 4: Advanced Machine Learning & Deep Learning (Weeks 13–16)

Month 5: Applied AI Domains & Industry Projects (Weeks 17–20)

Month 6: Career Readiness & Launch (Weeks 21–24)

Why AI & ML is the Future?

Why is the Viewsoft Academy Program the Right One?

Who is to take part in this Program?

The Ultimate Guide to Highest Certifications and your future.

Why This is the Opportunity You can not Afford to Miss.

Frequently Asked Questions on The Professional AI & ML Course By Viewsoft Academy

Fill Out the Form Below to Get All the Details!

We don’t send spam so don’t worry.

Capability Statement