Raj Cloud Technologies
AI & Machine Learning

AI & Machine Learning

Machine Learning is no longer a niche — every product company needs ML engineers, and the role pays ₹12-30 LPA at mid-level. This two-month program takes you from ML fundamentals through production deployment: regression, classification, clustering, ensembles, deep learning (CNN, RNN, LSTM, transformers), NLP, computer vision, and MLOps with FastAPI + Docker + AWS SageMaker. Strong on mathematical foundations + Python implementation, with portfolio projects across healthcare, finance, and retail that show you can ship real models — not just notebooks.

0 lessons

What you'll learn

  • Implement every major ML algorithm from scratch in Python
  • Train deep learning models in PyTorch + TensorFlow with confidence
  • Build NLP pipelines with transformers (BERT, GPT) + Hugging Face
  • Apply computer vision techniques to classification + detection problems
  • Forecast time-series data with ARIMA, Prophet, and LSTM
  • Deploy ML models as production APIs with FastAPI + Docker
  • Track experiments + version models with MLflow
  • Pass ML technical interviews at top product companies
  • Land Machine Learning Engineer roles paying ₹10-30 LPA

Technologies Taught

Python — NumPy, Pandas, Matplotlib, scikit-learnClassical ML — regression, classification, clustering, ensemblesDeep learning — TensorFlow + PyTorch + KerasNLP — transformers, BERT, Hugging FaceComputer Vision — CNNs, OpenCV, image classification + detectionTime-series — ARIMA, Prophet, LSTM forecastingMLOps — FastAPI, Docker, AWS SageMaker, MLflowVector databases for semantic searchStatistics + linear algebra + calculus for ML

Training Unique Features

  • Mathematical foundations explained from first principles — not just hand-wavy
  • Hands-on with real datasets across healthcare, finance, and retail
  • Build + deploy production models with FastAPI + Docker
  • Daily 90-minute live coding sessions with debugging walkthroughs
  • Deep learning labs run on GPUs — real model training, not toy examples
  • Mock interview rounds covering ML coding + system design
  • Portfolio projects you can confidently show in interviews
  • Trained by ML engineers with 10+ years of production experience
  • Resume + LinkedIn + GitHub polish for ML roles
  • Direct referrals to product companies actively hiring ML engineers

You Can Work As

Machine Learning EngineerData ScientistDeep Learning EngineerNLP EngineerComputer Vision EngineerAI Research EngineerML Ops Engineer

Upcoming In-Demand Jobs

LLM EngineerML Platform EngineerAI Safety EngineerMulti-Modal ML Engineer

Training Curriculum

Foundations (Statistics + Python)

3 topics
  • •AI/ML project lifecycle
  • •Roles in AI projects (Data Analyst, ML Engineer, AI Engineer)
  • •Data types in real-world projects (structured vs unstructured)

Python

5 topics
  • •Python Basics and Programming Fundamentals
  • •Data Types, Control Structures, and Functions
  • •Functions
  • •Loops & conditions with dataset use cases
  • •Working with NumPy

Data Visualization

1 topics
  • •Scatter plots with ML interpretation

Statistics

6 topics
  • •Descriptive Statistics
  • •Probability Concepts
  • •Data Distributions
  • •Inferential Statistics
  • •Hypothesis Testing
  • •Correlation and Regression Analysis

Data Handling & Exploratory Analysis

4 topics
  • •Data Cleaning and Preparation
  • •Handling Missing Values and Outliers
  • •Feature Scaling and Encoding
  • •Exploratory Data Analysis (EDA)

Machine Learning (ML)

4 topics
  • •Introduction to Machine Learning
  • •Supervised vs Unsupervised Learning
  • •ML lifecycle & workflow
  • •Model building intuition (how models actually learn)

Regression

2 topics
  • •Regression Algorithms
  • •Regression evaluation: MSE, RMSE, R² with hands-on examples

Classification

6 topics
  • •Classification Algorithms
  • •Classification evaluation: Confusion Matrix, Precision, Recall, F1-score
  • •Feature selection techniques
  • •Overfitting vs underfitting (with examples)
  • •Model Evaluation Metrics
  • •Cross-Validation and Hyperparameter Tuning

Unsupervised Learning

5 topics
  • •Clustering Algorithms
  • •K-Means clustering with Elbow Method
  • •Distance metrics (Euclidean, Manhattan – basic)
  • •Real-world clustering use cases
  • •Mini project on clustering

Advanced Machine Learning

4 topics
  • •Decision Trees and Random Forest
  • •Ensemble Learning Techniques
  • •Support Vector Machines (SVM)
  • •Dimensionality Reduction (PCA, LDA)

Natural Language Processing (NLP)

11 topics
  • •NLP pipeline (data → model → evaluation)
  • •Text Preprocessing Techniques
  • •Text cleaning with real datasets
  • •Tokenization, stemming vs lemmatization
  • •Feature Extraction (BoW, TF-IDF)
  • •Word Embeddings
  • •Word clouds
  • •Text Classification
  • •Sentiment Analysis
  • •End-to-end sentiment analysis project
  • •NLP mini project before capstone

Deep Learning

9 topics
  • •Introduction to Neural Networks
  • •Perceptron model
  • •Activation and Loss Functions
  • •Forward & backward propagation intuition
  • •Backpropagation and Optimization
  • •ANN build using Keras (step-by-step)
  • •Epochs, batches, optimizers in practice
  • •Performance improvement case study
  • •Recurrent Neural Networks (RNN, LSTM, GRU)

Computer Vision

5 topics
  • •Convolutional Neural Networks (CNN)
  • •CNN architecture breakdown
  • •MNIST / Image classification project
  • •Activation maps & feature extraction
  • •Transfer learning mini project

Advanced NLP & Transformers

8 topics
  • •Sequence Models
  • •Attention Mechanism
  • •Transformers and BERT
  • •Hugging Face setup & usage
  • •Transformer fine-tuning steps
  • •Text classification using BERT
  • •Text Summarization and Language Modeling
  • •Evaluation best practices for transformers

Generative AI & Advanced AI

3 topics
  • •Generative AI Fundamentals
  • •Large Language Models (LLMs)
  • •Prompt Engineering

Capstone Project

4 topics
  • •Problem Definition and Data Understanding
  • •End-to-End Model Development
  • •Model Evaluation and Interpretation
  • •Final Presentation and Documentation

Training Instructed By

MV
Mr. Vinay G---

AI & Data Science trainer with 10+ years in EdTech, specializing in hands-on, outcome-based programs. He simplifies complex AI/ML concepts for diverse learners and designs industry-ready curriculum using tools like Python, SQL, and Tableau, empowering career transitions and real-world learning in technical education. Approved trainer by Raj Cloud Technologies.

Approved trainer by Raj Cloud Technologies

Training content

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₹21,999

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