Machine Learning & Deep Learning
Defend Every Layer –
Next Cohort
Course Duration
160 Hrs
Course Overview
The Machine Learning & Deep Learning program is an advanced, industry-aligned course tailored for aspiring data scientists, AI engineers, and analytics professionals. This course covers the full spectrum of ML & DL topics, from foundational mathematics and statistics to the most cutting-edge deep learning architectures used in image recognition, natural language processing, and predictive modeling.
Key Features
- Covers both machine learning & deep learning
- Industry-aligned tools: Scikit-learn, TensorFlow, PyTorch
- Feature engineering & interpretability techniques
- Hyperparameter tuning & model optimization
Skills Covered
- Decision trees, random forests, boosting
- Transfer learning and batch normalization
- TensorFlow and PyTorch programming
- Time series modeling (ARIMA, Prophet)
- Neural networks fundamentals
- Feature engineering and pipelines
Course Curriculum
Machine Learning & Deep Learning
Module 1 - Mathematics & Statistics for Machine Learning
- Hypothesis Testing Understand how to make data-driven decisions using statistical hypothesis testing.Learn concepts like null/alternative hypotheses and significance levels.
- Chi-Square Testing Use the chi-square test to assess relationships between categorical variables. Helpful in feature selection and A/B testing.
- IQR(Interquartile Range) Analyze data spread and detect outliers using IQR. Learn how to visualize distribution with box plots.
- Range and central tendency Explore basic statistics like mean, median, mode, and range. Build a foundation for analyzing data distribution and variability.
- P-value Learn how to interpret p-values to determine statistical significance in hypothesis testing. Essential for model validation.
- Linear Algebra Understand vectors, matrices, and transformations—core mathematical tools behind ML models, especially deep learning.
- Calculus Learn basic derivatives and gradients used in model optimization and backpropagation in machine learning.
- Matrices Explore matrix operations like multiplication, inversion, and eigenvalues—key to representing and computing in ML algorithms.
Module 2 - Machine Learning Fundamentals
- Supervised learning (Regression, Classification) Learn to train models on labeled data to predict outcomes using regression and classification techniques. Understand real-world use cases like price prediction and sentiment analysis.
- Scikit-learn Use Python’s most popular ML library to build, train, and evaluate models. Learn tools for preprocessing, model selection, and performance evaluation.
- Model metrics Evaluate model performance using metrics like accuracy, precision, recall, F1-score, and RMSE. Choose the right metric based on problem type.
- Unsupervised Explore clustering and dimensionality reduction techniques like K-Means and PCA. Discover hidden patterns in data without predefined labels.
- Reinforcement machine learning Understand how agents learn optimal actions through rewards and penalties. Apply concepts like Q-learning and Markov Decision Processes.
- Feature engineering Transform raw data into meaningful features that boost model accuracy. Learn encoding, scaling, interaction features, and more.
- Ensemble models Combine multiple models to improve accuracy using methods like bagging, boosting, and stacking. Explore Random Forest, XGBoost, and others.
- Pipelines Build reusable ML workflows with Scikit-learn Pipelines. Chain together preprocessing and modeling steps for clean, efficient code.
- Cross-validation Improve model generalization by validating it on different data subsets. Learn k-fold cross-validation and train-test-split strategies.
Module 3 - Advanced Machine Learning
- XGBoost Learn XGBoost, a powerful gradient boosting algorithm known for speed and accuracy. Widely used in ML competitions and real-world applications.
- CatBoost Explore CatBoost, a gradient boosting library optimized for categorical features. Efficient, scalable, and less sensitive to parameter tuning.
- Hyperparameter tuning Improve model performance by optimizing hyperparameters using techniques like Grid Search and Random Search with cross-validation.
- Model interpretability Understand how models make predictions using tools like SHAP, LIME, and feature importance. Essential for building trust in ML decisions.
Module 4 - Time Series Forecasting & Analysis
- Trend/seasonality Identify long-term trends and recurring seasonal patterns in time series data. Essential for accurate forecasting and business planning.
- Moving average Smooth out short-term fluctuations to reveal trends using rolling averages. Used in signal processing and stock market analysis.
- ARIMA (AutoRegressive Integrated Moving Average) Model time series data with trends and noise using ARIMA. Combines autoregression, differencing, and moving averages for powerful forecasting.
- SARIMA(Seasonal ARIMA) Extend ARIMA to handle seasonal patterns explicitly. Ideal for monthly, quarterly, or cyclic data with repeating trends.
- Prophet(by Meta) Use Facebook Prophet for quick, flexible time series forecasting. Handles seasonality, holidays, and missing data with ease.
- Seasonality decomposition Break down time series into trend, seasonality, and residual components. Helps visualize and analyze each influence separately.
Module 5 - Deep Learning Applications
- Image classification Train deep learning models to recognize and categorize images using CNNs (Convolutional Neural Networks). Applied in areas like facial recognition and medical imaging.
- Sentiment analysis Use NLP and deep learning to determine the sentiment (positive, negative, neutral) behind textual data such as reviews, tweets, or feedback.
- Tabular DL models Apply deep learning techniques to structured/tabular data using models like TabNet. Optimize performance on datasets typical in business and finance.
- Transfer learning Leverage pre-trained models to accelerate training and improve accuracy on smaller datasets. Useful in computer vision and NLP tasks.
- Batch norm Improve training speed and stability by normalizing inputs across layers. Helps reduce internal covariate shift in deep networks.
- Dropout Prevent overfitting by randomly disabling neurons during training. Enhances generalization by promoting model robustness.
- Optimizers Learn how optimizers like SGD, Adam, and RMSProp adjust weights during training to minimize loss and improve convergence.
- Loss functions Measure model prediction error using functions like Cross-Entropy or MSE. A crucial part of training and guiding model learning.
Module 6 - Neural Networks
- Neurons Understand the basic computational unit of neural networks, inspired by the human brain. Learn how neurons process inputs to produce activations.
- Perceptron Explore the simplest type of neural network used for binary classification. Learn how perceptrons form the basis for deeper architectures.
- Activation functions Use functions like ReLU, Sigmoid, and Tanh to introduce non-linearity in models. Crucial for learning complex patterns in data.
- Gradient descent Master the optimization algorithm that minimizes model error by adjusting weights. Understand learning rate, cost function, and convergence.
- Convolutional Neural Networks (CNNs) Build models for image processing using convolutional layers, pooling, and filters. Widely used in visual recognition tasks.
- Recurrent Neural Networks (RNNs) Basics Understand how RNNs handle sequential data like text or time series. Learn about loops, memory, and limitations like vanishing gradients.
- TensorFlow/PyTorch setup Set up the two most widely used deep learning frameworks. Learn environment setup, GPU use, and writing your first model.
- Hypothesis Testing
- Chi Testing
- Iqr
- Range and central tendency
- P-value
- Linear Algebra
- Calculus
- Matrices
- Supervised learning (Regression, Classification)
- Scikit-learn
- Model metrics
- Unsupervised
- Reinforcement machine learning
- Feature engineering
- Ensemble models
- Pipelines
- Cross-validation
- XGBoost
- CatBoost
- Hyperparameter tuning
- Model interpretability
- Trend/seasonality
- Moving average
- ARIMA
- SARIMA
- Prophet
- Seasonality decomposition
- Image classification
- Sentiment analysis
- Tabular DL models
- Transfer learning
- Batch norm
- Dropout
- Optimizers
- Loss functions
- Neurons
- Perceptron
- Activation functions
- Gradient descent
- CNN
- RNN basics
- TensorFlow/PyTorch setup
Salary Scale
Maximum
35 LPA
Average
15 LPA
Minimum
10 LPA
Job Role
- Machine Learning Engineer
- Data Scientist
- AI Engineer
- AI Product Developer
- Business Intelligence Engineer
- Deep Learning Researcher
Course Certificate
Eligible Criteria
- B.E/B.Tech in ECE, EEE, Instrumentation (Final Year or Recent Graduates)
-
Possess good English communication skills
Tools & Technologies
Training Options
Online
Training
₹
20,000
Including GST*
-
24/7 LMS Access
-
Live Online Session
-
On-Campus Immersion
Classroom
Training
₹
40,000
Including GST*
-
24/7 LMS Access​
-
Peer Learning & Support
-
Career Guidance & Mentorship
Why Join this Program
Full-Stack ML + DL Curriculum
Covers everything from basic models to neural networks.
Code + Concepts
Learn both theoretical foundations and real implementation.
High-Paying Roles
ML & DL are among the top-paying domains in tech today.
AI-First Future Ready
Future-proof your career in the age of artificial intelligence.
FAQ
Basic Python is helpful but covered initially.
Yes. TensorFlow and PyTorch both included.
Yes. Multiple real-life datasets and scenarios are used.
Absolutely — suitable for both industry and academia.
Yes – ARIMA, SARIMA, Prophet, etc.
Yes – model deployment strategies are covered.