Mechine Learning Courses Online

✅ Learn ML Online from experts working in top tech companies
✅ Learn ML from scratch or only a portion that matters to you
✅ Career mentorship, resume support, and mock interviews etc.,
✅ Real-world, hands-on projects to build your portfolio
✅ Industry-recognized certifications

Join Now – Pay After You Learn

Learn First, Pay Later Pay Only if Satisfied Why Risk Your Hard Earned Money

10 Reason to Learn Machine Learning

ML professionals earn between ₹8–₹28 LPA in India, with top roles in tech, finance, and healthcare offering even more.

Over 1.1 crore AI/ML/Data Science jobs are expected in India by 2026 — and demand keeps rising across industries.

ML powers major platforms like Google Search, Amazon recommendations, Netflix, and self-driving cars — skills that are in high demand.

ML offers fast-track career growth, and is one of the most popular domains for IT professionals, analysts, and engineers looking to switch.

From diagnosing diseases to detecting fraud, ML is solving real problems and improving lives — making your skills truly meaningful.

ML is the stepping stone to mastering advanced fields like Deep Learning, Natural Language Processing (NLP), and Generative AI.

Whether it’s banking, biotech, retail, education, or logistics — ML is everywhere. Learn once, work anywhere.

With open-source libraries like Scikit-learn, TensorFlow, and PyTorch, it’s easier than ever to build, test, and deploy ML models.

ML is not a passing trend — it’s at the core of digital transformation, automation, and the future of work.

Learn how machines learn, and start building smart applications — from recommendation engines to AI chatbots and forecasting tools.

PLACEMENT RECORD

We’re proud of our alumni who now work at top companies. Although we keep their names private, we’re happy to share details if you request.

Tech Mahindra
Tech Mahindra
Haldor Topsoe
Haldor Topsoe
Altron
Altron
Ford
Ford
Donaldson
Donaldson
ITC Infotech
ITC Infotech
Icube logics
Icube logics
Autodesk
Autodesk
Tata Technologies
Tata Technologies
Feaurecia
Feaurecia
L & T Technology
L & T Technology
Kennametal
Kennametal
NX customization Training
ASM Technologies
NX Donaldson Prasanna
Donaldson

Syllabus Basics

  • What is Machine Learning?
  • Evolution of Machine Learning
  • Importance & Impact of ML in the real world
  • History and Growth of ML
  • ML vs Traditional Programming
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
  • Key Concepts: Features, Labels, Training, Testing
  • Bias-Variance Trade-off
  • Underfitting vs Overfitting
  • ML Model Lifecycle
  • ML Pipeline Overview
  • Use Cases of ML in Industry
  • Linear Algebra Basics
  • Matrix Operations
  • Calculus for ML
  • Probability & Combinatorics
  • Basics of Statistics (Mean, Median, Mode, Variance)
  • Types of Data: Structured, Unstructured, Semi-structured
  • Dataset Characteristics: Volume, Velocity, Variety
  • Data Formats and Sources
  • Importance of Data Quality
  • Data Preprocessing Overview
  • Handling Missing Data
  • Encoding Categorical Variables
  • Normalization vs Standardization
  • Train-Test Split
  • Feature Scaling Techniques

Syllabus Intermediate

  • Linear Regression: Simple, Multiple
  • Assumptions of Regression Models
  • Evaluation Metrics: MAE, MSE, RMSE, R²
  • Polynomial Regression
  • Regularization: Ridge, Lasso
  • Logistic Regression
  • Decision Trees for Classification
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Support Vector Machines (SVM)
  • ROC, AUC, Precision, Recall, F1-Score
  • Clustering Concepts
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN
  • Dimensionality Reduction: PCA, t-SNE
  • Feature Selection Techniques
  • Feature Extraction
  • Binning, Interaction Features
  • Handling Outliers
  • Recursive Feature Elimination
  • Cross-Validation Techniques
  • K-Fold, Stratified K-Fold
  • Bias-Variance Analysis
  • Hyperparameter Tuning with GridSearchCV and RandomSearchCV
  • Confusion Matrix Analysis
  • Bagging vs Boosting
  • Random Forest
  • Gradient Boosting Machines (GBM)
  • AdaBoost
  • XGBoost & LightGBM
  • Stacking and Voting Classifiers

Syllabus Advanced

  • Introduction to Model Deployment
  • Saving/Loading Models (Pickle, Joblib)
  • Creating APIs with Flask/FastAPI
  • Dockerization of ML Models
  • CI/CD in ML Projects
  • Text Cleaning and Preprocessing
  • Tokenization, Lemmatization, Stop Words
  • Vectorization Techniques: Count Vectorizer, TF-IDF
  • Sentiment Analysis
  • Named Entity Recognition
  • Text Classification Models
  • Time Series Basics
  • Components of Time Series
  • Stationarity, ACF & PACF
  • ARIMA, SARIMA Models
  • LSTM for Time Series
  • Forecasting Projects
  • Content-Based Filtering
  • Collaborative Filtering
  • Hybrid Models
  • Matrix Factorization Techniques
  • Building Movie/Product Recommendation Engines
  • Artificial Neural Networks (ANN)
  • Forward Propagation & Backpropagation
  • Activation Functions
  • Loss Functions
  • Optimization Techniques (SGD, Adam)
  • CNNs for Image Recognition
  • RNNs for Sequential Data
  • LSTM & GRU Architectures
  • Attention Mechanism
  • Transformers Overview (BERT, GPT)
  • Introduction to MLOps
  • ML Lifecycle Management
  • Model Versioning & Monitoring
  • MLflow & DVC
  • Scaling ML with Cloud (AWS, GCP, Azure)
  •  

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FAQ

Yes! We offer the first few classes completely free. You can explore the course, experience our teaching style, and decide later if you’d like to upgrade to the advanced modules or project-based training.

Just click on “Start Free Classes Now” and register. You’ll instantly get access to the free content and updates about upcoming sessions. No credit card or payment required to begin.

The introductory classes are free. You only pay for advanced modules or project training. Fees are affordable and structured per module. [Download the brochure] or [Contact us] for the latest pricing.

Basic Python knowledge is helpful. If you’re new, we offer foundational Python training to get you up to speed before diving into ML.

You can apply for roles such as:

  • Machine Learning Engineer

  • AI/ML Analyst

  • Data Scientist

  • ML Researcher

  • ML Ops Engineer

Yes. We focus on project-based learning. You’ll build real-time projects to understand practical implementation.

Absolutely! We provide doubt-clearing sessions and post-class support via chat, calls, and email.

Yes, a course completion certificate will be provided, which is helpful for your resume and job interviews.

  • You’ll use:

    • Python

    • Jupyter Notebook / Google Colab

    • Scikit-learn, Pandas, NumPy

    • VS Code or Anaconda
      All are free and beginner-friendly.

We recommend 6–10 hours per week, depending on your pace and familiarity with concepts.

Definitely. The course is designed for career changers, offering foundational to advanced ML training along with resume-building and interview prep.

Yes, we specialize in helping businesses find and hire the right talent. Our HR and recruitment services are tailored to meet your company’s specific hiring needs. Contact us to learn how we can support your hiring process.
Please visit our page : WoCons – Custom AI & CAD Solutions | Bengaluru