Machine Learning - Academy of Tech Masters

Machine Learning

AI Training equips learners with the skills to design, build, and deploy intelligent systems using machine learning, deep learning, and data-driven techniques, enabling automation, advanced decision-making, and innovative solutions across diverse domains.

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Artificial Intelligence Course Curriculum

It stretches your mind, think better and create even better.

MATHEMATICS & STATISTICS For AI

Module 1: Linear Algebra for ML

Topics:

1.1Matrix Operations

Matrix Multiplication and Properties

Eigenvalues and Eigenvectors

Singular Value Decomposition

Matrix Factorization Techniques

1.2 Applications in ML

Principal Component Analysis (PCA)

Linear Discriminant Analysis

PageRank Algorithm

Recommendation Systems

Module 2: Calculus & Optimization

Topics:

2.1 Differential Calculus

Gradients and Partial Derivatives
Chain Rule for Backpropagation
Jacobian and Hessian Matrices
Taylor Series Approximation

2.2 Optimization Algorithms

Gradient Descent Variants
Newton’s Method
Conjugate Gradient
Stochastic Optimization
Constrained Optimization

Module 3: Probability & Statistics

Topics:

3.1 Probability Theory

Probability Distributions

Joint and Conditional Distributions

Bayesian Probability

Markov Chains

3.2 Statistical Inference

Maximum Likelihood Estimation

Bayesian Inference

Hypothesis Testing

Confidence Intervals

MCMC Methods

Module 4: Statistical Analysis

Topics:

4.1 Advanced Storage Features

Storage Account Security (SAS, RBAC, Firewall)
Lifecycle Management Policies
Azure File Sync Implementation
Storage Migration Service
StorSimple and Data Box

4.2 Data Protection and Compliance

Encryption at Rest and in Transit
Customer-Managed Keys (CMK)
Immutable Storage
Legal Hold and Retention Policies
GDPR and Compliance Features

Module 5: Exploratory Data Analysis

Topics:

5.1 EDA Methodology

Data Profiling
Pattern Discovery
Correlation Analysis
Anomaly Detection

5.2 Feature Engineering

Feature Creation

Feature Selection

Feature Transformation

Dimensionality Reduction

Feature Importance Analysis

MACHINE LEARNING MASTERY

Module 6: ML Fundamentals

Topics:

6.1 ML Pipeline Development

Problem Formulation

Data Preparation

Model Selection

Evaluation Strategies

Deployment Planning

6.2 Model Selection & Validation

Cross-Validation Techniques

Hyperparameter Tuning

Ensemble Methods

AutoML Tools

Module 7: Supervised Learning

7.1 Classification Algorithms

Logistic Regression

Support Vector Machines

Decision Trees & Random Forests

Gradient Boosting (XGBoost, LightGBM)

Neural Networks for Classification

7.2 Regression Analysis

Linear Regression Models

Polynomial Regression

Ridge, Lasso, Elastic Net

Non-linear Regression Models

Time Series Forecasting

Module 8: Unsupervised Learning

Topics:

8.1 Clustering Algorithms

K-Means and Hierarchical Clustering

DBSCAN and Density-Based Methods

Gaussian Mixture Models

Spectral Clustering

8.2 Dimensionality Reduction

PCA and ICA

t-SNE and UMAP

Autoencoders

Feature Selection Methods

Module 9: Deep Learning Foundations

Topics:

9.1 Neural Network Architecture

Feedforward Networks

Activation Functions

Backpropagation Algorithm

Optimizers and Learning Rate Scheduling

9.2 Training Deep Networks

Weight Initialization

Batch Normalization

Dropout and Regularization

Transfer Learning

Module 10: Advanced Deep Learning

Topics:

10.1 Convolutional Neural Networks

CNN Architectures (ResNet, EfficientNet)

Image Classification

Object Detection (YOLO, R-CNN)

Semantic Segmentation

10.2 Recurrent Networks & Transformers

LSTM and GRU Networks

Attention Mechanisms

Transformer Architecture

Vision Transformers

Free

Free access this course

Level

Intermediate
Total Enrolled

1
Last Updated

August 29, 2025

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A course by

RT

Ravi Teja

T

Tutor