DATADRIX : Machine Learning

Harnessing the Potential of Machine Learning

A comprehensive program designed to provide in-depth knowledge of machine learning concepts, algorithms & applications. Explore supervised & unsupervised learning techniques, delve into regression, classification & clustering algorithms & understand the fundamentals of model evaluation and optimization. Develop practical skills through hands-on projects, learn to implement machine learning models using popular frameworks & gain the expertise to solve real-world problems. By enabling machines to adapt and improve over time, machine learning is driving innovation across various industries, from healthcare to finance, revolutionizing the way we process information, make predictions, and solve complex problems.

Course Curriculum

A syllabus is a meticulously crafted document that serves as a comprehensive roadmap for the training program. It plays a pivotal role in guiding candidate along their learning journey, offering a structured framework for acquiring knowledge and honing skills.

Module 1

  • Introduction to Python Programming
  • Variable & Datatypes
  • Conditional & Looping Statements
  • Functions & File Handling
  • Exception Handling & Threading
  • Searching & Sorting
  • Object Oriented Programming
  • Python Libraries
  • Handling structural Datasets
  • Data Manipulation
  • Data Cleaning
  • Importing data from mutiple sources
  • Finding Insights from datasets

In our Python course, we delve into the powerful ecosystem of Python libraries that enhance the functionality and efficiency of your coding projects. Students will explore widely-used libraries such as NumPy for numerical computations, Pandas for data manipulation and analysis, and Matplotlib and Seaborn for data visualization. Additionally, the course covers libraries like Scikit-Learn for machine learning, BeautifulSoup for web scraping, and Flask for web development. Each library is introduced with practical examples and hands-on exercises, enabling students to understand their applications and integrate them into their projects. By mastering these libraries, participants will be equipped with the tools needed to tackle complex programming challenges and develop sophisticated applications, making them valuable assets in the field of software development.

  • Introduction to SQL and MySQL
  • Data Creation and Retrieval
  • Data Filtering
  • Data Analysis using aggregate functions and group by
  • Joins and Keys
  • MySQL Joins
  • Subqueries and Views
  • Window/Analytical Functions
  • Case Study

Module 2

  • What is Data Visualization?
  • Data Visualization in Python
  • Matplotlib and Seaborn
  • Line Charts & Bar Graphs
  • Histograms, Scatter Plots & Heat Maps

Data cleaning is a crucial part of our Python course, focusing on the essential techniques required to prepare raw data for analysis. This section of the course teaches students how to handle missing values, detect and correct errors, and ensure consistency in datasets using powerful Python libraries such as Pandas and NumPy. Students will learn to identify outliers, standardize data formats, and manage duplicate records, gaining hands-on experience with real-world datasets. The curriculum emphasizes practical skills through projects and exercises, enabling students to transform messy data into clean, reliable datasets ready for analysis. By mastering data cleaning, students will be well-equipped to tackle data-driven challenges and contribute effectively to data science and analytics projects.

  • Introduction to Data Cleaning and Data Types
  • Exploring and Visualization the missing values
  • Advanced-Data Cleaning Concepts
  • Introduction to Feature Engineering
  • Feature Extraction and Transformation
  • Feature Selection and Dimensionality Reduction
  • Linear Algebra
  • System of equations as line
  • Determinants & Matrix
  • Linear dependence and independence
  • Dot product & cross Product
  • Matrix as linear transformation
  • Matrix inverse
  • Eigen values & eigen vectors
  • Probabiity & Statistics
  • Gradient Descent
  • Regularization techniques
  • Local minima & Global Minima
  • Bayes Theorem
  • MSE , MAE & RMSE
  • Discrete & continuos functions
  • Understanding Data Models: Data modeling in Power BI involves structuring raw data into a format that is optimized for analysis. It includes defining tables, relationships, and data types to enable efficient querying and reporting.
  • Data Relationships: Establish relationships between different tables in your data model. Power BI supports one-to-many, one-to-one, and many-to-many relationships, enabling seamless interaction between data points.
  • Star and Snowflake Schemas: Learn about designing data models using Star Schema (with fact and dimension tables) or Snowflake Schema (where dimension tables are normalized), which help improve performance and scalability in reporting.
  • Data Transformation: Power BI’s Power Query allows users to clean and transform raw data before it enters the data model. Use M language to reshape and prepare the data for analysis.
  • Calculated Columns & Measures: Understand the difference between calculated columns (computed at the data row level) and measures (aggregations such as sum, average, etc., computed on-demand). Both are powered by DAX (Data Analysis Expressions).
  • Data Types and Formatting: Ensure that the correct data types (e.g., integer, decimal, text, date) are assigned to columns for accurate calculations and analysis.
  • Data Normalization and Denormalization: Explore when to normalize (split data into multiple tables to reduce redundancy) or denormalize (combine tables to improve query performance) depending on the report’s needs.
  • Performance Optimization: Learn techniques to optimize data models, such as reducing the size of your data, removing unnecessary columns, creating summary tables, and applying relationships wisely.
  • DirectQuery vs Import Mode: Understand the difference between DirectQuery (real-time data from source) and Import Mode (loading data into Power BI), and when to use each mode for optimal performance.

Module 3

Machine Learning (ML) is a branch of artificial intelligence (AI) that focuses on the development of algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed. This course provides an introduction to the fundamental concepts and techniques of machine learning, empowering you to understand and apply various models to solve real-world problems. Throughout the course, you will explore supervised and unsupervised learning, data preprocessing, model evaluation, and popular algorithms such as regression, classification, and clustering. By the end of the course, you’ll have hands-on experience with machine learning tools and libraries, enabling you to implement predictive models and contribute to the growing field of data science.

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Predicts continuous values: Regression models are used to predict real-numbered outputs such as prices, temperatures, or sales.
  • Linear vs Non-linear models: Includes techniques like linear regression, polynomial regression, and support vector regression (SVR) to capture relationships between variables.
  • Loss functions: Measures like Mean Squared Error (MSE) or Mean Absolute Error (MAE) are used to evaluate the accuracy of regression models.
  • Use cases: Common applications include forecasting, trend analysis, and predictive analytics (e.g., stock prices, house prices).
  • Predicts categorical outcomes: Classification models aim to predict discrete categories or labels, such as spam/not spam, disease/healthy, etc.
  • Binary vs Multi-class classification: Classification problems can involve two classes (binary classification) or multiple classes (multi-class classification).
  • Evaluation metrics: Metrics like accuracy, precision, recall, F1-score, and confusion matrix are used to assess classification models.
  • Use cases: Popular in areas such as fraud detection, image recognition, email spam filtering, and medical diagnosis.
  • Accuracy: Measures the percentage of correctly predicted instances out of the total predictions. Useful when classes are balanced, but not ideal for imbalanced datasets.
  • Precision and Recall: Precision evaluates the number of true positive predictions compared to all positive predictions, while recall measures true positives compared to actual positive instances. Essential for problems where false positives/negatives are critical (e.g., medical diagnosis).
  • F1-Score: The harmonic mean of precision and recall. It provides a balanced measure for classification models, especially when dealing with imbalanced datasets.
  • Confusion Matrix: A table used to describe the performance of a classification model, showing the counts of true positives, true negatives, false positives, and false negatives.
  • ROC-AUC (Receiver Operating Characteristic – Area Under Curve): A curve that plots the true positive rate against the false positive rate, with the AUC score representing the model’s ability to distinguish between classes.
  • Mean Squared Error (MSE) and Root Mean Squared Error (RMSE): Common metrics for regression models, indicating the average squared difference between predicted and actual values, with RMSE providing a more interpretable scale.
  • Definition: Occurs when a model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and testing datasets.
  • Symptoms: Low accuracy and high error rates on both training and testing sets.
  • Causes: Can result from overly simplistic models (e.g., linear models for complex problems), insufficient features, or inadequate training.
  • Solutions: Use more complex models, add more features, or allow the model to train longer.
  • Definition: Happens when a model is too complex and learns not only the patterns in the training data but also the noise, leading to poor generalization on new, unseen data.
  • Symptoms: High accuracy on the training set but poor performance on the testing set.
  • Causes: Complex models (e.g., deep neural networks with too many layers), too few data points, or training for too many iteration
  • Solutions: Apply regularization techniques (like L1 or L2), simplify the model, use cross-validation, or increase the amount of training data.

Balancing Models:

The goal is to find the right model complexity to avoid both underfitting and overfitting, enabling good generalization to unseen data.

  • Hyperparameter Tuning: Adjusting model parameters such as learning rate, batch size, or number of layers (e.g., grid search, random search).
  • Regularization: Adding penalties to prevent overfitting (e.g., L1, L2 regularization, dropout for neural networks).
  • Feature Engineering: Selecting and transforming relevant features to improve the model’s ability to learn from data.
  • K-Fold Cross-Validation: The dataset is split into K parts, and the model is trained on K-1 parts and validated on the remaining one. This process is repeated K times, and results are averaged.
  • Stratified Cross-Validation: Ensures that each fold has a proportional representation of different classes, useful for imbalanced datasets.
  • Leave-One-Out Cross-Validation: Each data point is used as a test case once while the rest is used for training.
  • Definition: A technique used to address class imbalance by reducing the number of samples in the majority class so that it matches the size of the minority class.
  • Process: Randomly removing data points from the majority class to balance the dataset.
  • Reduces the size of the dataset, making training faster.
  • Helps models focus more on the minority class, which is often more important in imbalanced datasets.
  • May lead to loss of important information, as valuable samples from the majority class are discarded.
  • Can result in underfitting, where the model is unable to learn enough from the data.
  • A technique to balance class distribution by increasing the number of samples in the minority class, either by duplicating existing samples or generating new ones.
  • Random Oversampling: Simply duplicates examples from the minority class.
  • SMOTE (Synthetic Minority Over-sampling Technique): Generates synthetic examples by interpolating between existing minority class examples.
  • Ensures the model has enough data from the minority class, improving the model’s ability to recognize those examples.
  • Helps reduce bias toward the majority class, especially in imbalanced datasets.
  • May lead to overfitting, especially when the same minority class samples are duplicated multiple times.
  • Increases the size of the dataset, which may lead to longer training times.

Module 4

  • Defination: The process of transforming raw data into meaningful features that can enhance the performance of machine learning models.
  • Creating new features: Generating new features from existing ones, like combining two features or applying mathematical operations.
  • Encoding categorical data: Using techniques like one-hot encoding or label encoding to transform categorical variables into numerical formats.
  • Normalization/Standardization: Scaling features so they are comparable, especially important for algorithms sensitive to feature scaling (e.g., KNN, SVM).
  • Handling missing values: Filling in missing values through methods like imputation or removing features/rows with too many missing data points.
  • Improves model performance by providing more relevant and refined data.
  • Helps capture important patterns or relationships within the data.
  • Time-consuming and requires domain expertise.
  • Incorrect feature engineering can introduce noise and reduce model performance.
  • Definition: The process of selecting the most relevant features for training a machine learning model while discarding irrelevant or redundant ones.
  • Filter Methods: Selecting features based on statistical techniques (e.g., correlation, chi-square tests, information gain).
  • Wrapper Methods: Selecting features based on model performance using techniques like forward selection, backward elimination, or recursive feature elimination (RFE).
  • Embedded Methods: Feature selection is integrated into the model itself (e.g., Lasso, Ridge regression, decision trees).
  • Reduces overfitting by eliminating irrelevant or redundant features.
  • Improves model performance and interpretability
  • Decreases computational complexity, speeding up training.
  • Can miss important features if not applied carefully.
  • Some methods are computationally expensive for large datasets.

Both bagging and boosting are powerful ensemble techniques that enhance model performance. Bagging is ideal when the goal is to reduce variance, while boosting excels at reducing bias, making it especially effective for complex datasets.

An ensemble learning technique that creates multiple independent models (typically decision trees) by training them on random subsets of data and then aggregating their predictions to improve accuracy and reduce variance.

  • Data Sampling: Randomly select subsets of the training data with replacement (bootstrap sampling)
  • Model Training: Train multiple models independently on each subset.
  • Aggregation: For classification, the final prediction is typically done by majority voting, and for regression, the average of the predictions is taken.
  • Key Algorithm: Random Forest is a popular algorithm based on bagging.
  • Reduces variance, making models less prone to overfitting.
  • Each model is trained independently, so it can run in parallel, improving computational efficiency.
  • Does not reduce bias if the individual models are biased.
  • Less effective on very small datasets as it relies on resampling.

An ensemble technique that sequentially trains models, where each subsequent model focuses on correcting the errors of the previous ones. The final prediction is a weighted combination of all models.

  • Sequential Learning: Train models one at a time, where each new model is trained to address the errors made by the previous models
  • Weighting: Each model is weighted based on its accuracy. Misclassified points get more weight in the next round of training.
  • Aggregation: The final prediction is a weighted sum (or vote) of the models’ outputs.
  • Key Algorithms: AdaBoost, Gradient Boosting, XGBoost.
  • Reduces bias by focusing on correcting errors from previous models
  • Works well on imbalanced datasets and complex problems.
  • More prone to overfitting, especially with noisy data.
  • Sequential nature makes it harder to parallelize, making it slower to train.

Comparison:

  • Bagging reduces variance by combining many independent models, while boosting reduces bias by focusing on sequentially correcting errors.
  • Bagging models are trained in parallel, while boosting models are trained sequentially, making bagging faster but boosting often more accurate in practice.
  • Unsupervised learning deals with datasets without labeled outcomes, where the goal is to find hidden patterns or intrinsic structures in the data.
  • Common types include clustering, association, and dimensionality reduction.
  • Market segmentation, anomaly detection, recommendation systems, etc.
  • Partitions data into K clusters based on the nearest centroid.
  • Key aspects: Initial centroids, distance calculation, cluster updating.
  • Builds a hierarchy of clusters using a tree-like structure (dendrogram).
  • Types: Agglomerative (bottom-up) and divisive (top-down).
  • Reduces high-dimensional data into fewer components while preserving variance.
  • Used to simplify data for visualization or to speed up machine learning algorithms.
  • Neural networks used to learn compressed, low-dimensional representations of data.
  • Identifying outliers or rare events in data, such as fraud detection or equipment failure.
  • Clustering-based (e.g., isolation forests), distance-based (e.g., k-nearest neighbors), or density-based (e.g., DBSCAN).
  • Grouping customers based on purchasing behaviors or demographics.
  • Identifying fraudulent transactions or network intrusions.

Natural Language Processing (NLP) is a pivotal area of artificial intelligence that enables computers to understand, interpret, and generate human language. This field encompasses various techniques and applications, such as text summarization, sentiment analysis, and machine translation. Essential to NLP are processes like text preprocessing, which involves tokenization, stopword removal, and vectorization methods like TF-IDF. Advanced word embeddings, such as Word2Vec, GloVe, and FastText, along with Transformer models like BERT and GPT, play a crucial role in converting text into meaningful numerical representations and context-aware embeddings.

In practical applications, NLP techniques are employed in tasks such as text classification, named entity recognition (NER), and part-of-speech (POS) tagging. For instance, sentiment analysis classifies text based on sentiment, while NER identifies entities such as people or locations within text. Part-of-speech tagging helps in understanding the grammatical structure of sentences. Machine translation systems, using models like GNMT and Transformer-based approaches, aim to translate text from one language to another, while chatbots leverage NLP for handling user interactions in a conversational manner.

The field also involves evaluating the performance of NLP models using metrics such as accuracy, precision, recall, F1 score, BLEU, and ROUGE. Challenges in NLP include managing ambiguities in language, understanding context, and dealing with language variability. Libraries and tools such as NLTK, spaCy, Hugging Face Transformers, and Gensim are instrumental in implementing and experimenting with NLP techniques. These advancements and tools are essential for developing robust NLP systems capable of handling real-world language processing tasks effectively.

Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are crucial techniques for dimensionality reduction in data analysis and machine learning. PCA is an unsupervised method that transforms data into a new coordinate system to maximize variance along the principal components. This helps in reducing the number of features while retaining the most significant aspects of the data, making it valuable for data visualization and noise reduction. PCA achieves this by calculating eigenvectors and eigenvalues of the data covariance matrix to project the data onto a lower-dimensional space.

On the other hand, LDA is a supervised technique aimed at finding a linear combination of features that maximizes class separability. It does so by calculating the mean vectors for each class and constructing scatter matrices to maximize the distance between class means while minimizing the variance within each class. LDA is particularly useful for classification tasks where maintaining class discrimination is crucial, such as in facial recognition or medical diagnostics. Unlike PCA, LDA takes class labels into account and focuses on enhancing class-specific feature extraction, though it assumes normally distributed data with identical class covariances.

Model Persistence and Deployment are critical steps in the machine learning lifecycle that ensure models can be used in real-world applications and remain effective over time.

Model Persistence involves saving a trained machine learning model to a storage medium so that it can be reloaded and used later without needing to retrain. This process typically includes serializing the model’s parameters and architecture into a file format that can be easily stored and retrieved. Common formats for persistence include joblib or pickle files in Python, and binary formats for other languages. The key advantage of model persistence is that it enables scalability and efficiency, allowing data scientists and engineers to deploy models in production environments without redundant computation. Proper persistence also ensures that models can be versioned and tracked over time, facilitating reproducibility and maintenance.

Model Deployment is the process of integrating a trained model into a production environment where it can make predictions on new, real-world data. Deployment strategies vary depending on the use case and include options such as cloud services (e.g., AWS SageMaker, Azure ML), on-premises servers, or embedded systems. The deployment process involves setting up the necessary infrastructure to handle model inference requests, ensuring that the model performs efficiently under operational conditions, and monitoring its performance for accuracy and reliability. Effective deployment requires consideration of aspects such as scalability, latency, and security to ensure that the model can deliver consistent and accurate predictions in production environments.

End-to-End Projects in machine learning and data science involve the complete lifecycle of a project, from initial problem identification and data collection to model deployment and performance monitoring. These projects demonstrate the ability to handle various stages of a data science pipeline, ensuring that the solution is robust, scalable, and effective.

1. End-to-End Sales Forecasting System:

  • Problem Identification: Develop a system to predict future sales based on historical data.
  • Data Collection: Gather sales data, including transaction history, seasonality, and marketing campaigns.
  • Data Preparation: Clean and preprocess data, handle missing values, and feature engineering.
  • Model Development: Implement and train forecasting models such as ARIMA, SARIMA, or machine learning algorithms like Random Forest or XGBoost.
  • Evaluation: Assess model performance using metrics like RMSE or MAE.
  • Deployment: Deploy the model on a cloud platform to generate and visualize forecasts.
  • Monitoring: Track model performance and retrain with new data as necessary.

2. End-to-End Customer Sentiment Analysis:

  • Problem Identification: Analyze customer reviews to gauge sentiment and identify areas for improvement.
  • Data Collection: Scrape or collect customer reviews from online platforms and social media.
  • Data Preparation: Clean and preprocess text data, including tokenization and removing stop words.
  • Model Development: Train sentiment analysis models using Natural Language Processing (NLP) techniques, such as sentiment classifiers using BERT or LSTM.
  • Evaluation: Use metrics like accuracy, precision, recall, and F1 score to evaluate model performance.
  • Deployment: Integrate the sentiment analysis model into a web application or dashboard for real-time review analysis.
  • Monitoring: Regularly update the model with new data and refine it to improve accuracy.

3. End-to-End Recommendation System:

  • Problem Identification: Build a recommendation engine to suggest products or content to users based on their preferences.
  • Data Collection: Collect user interaction data, such as clicks, ratings, and purchase history.
  • Data Preparation: Perform data cleaning, normalization, and feature extraction.
  • Model Development: Implement collaborative filtering, content-based filtering, or hybrid recommendation algorithms.
  • Evaluation: Assess the effectiveness of recommendations using metrics like precision, recall, and the mean average precision (MAP).
  • Deployment: Deploy the recommendation engine on a web or mobile platform, integrating it with user interfaces.
  • Monitoring: Continuously monitor the system’s performance and update recommendations based on user feedback and new data.

These projects illustrate a comprehensive approach to solving real-world problems by integrating data collection, preparation, modeling, and deployment, ensuring that the solution is practical and impactful.

Internship Program

This internship is a part of the course curriculum to help you gain real experience in the Data Science domain.During this internship, you will go through various challenges which you allow to explore new skills and push your limits while learning something new during the projects.

Topics Covered :

Integration of python & SQL

Web Scrapping

Data Cleaning with Python

Model Evaluation

Git / Github Integration

End to End Projects

Interview Preparation

Datadrix offers top-notch placement opportunities. With strong industry ties and modern training, we excel in placing our candidates. Our results speak to our commitment to shaping successful careers. Our approach ensures to open pathway for learners to achieve good growth in the domain

Activities Covered :

Interview Pattern Preparation

Mock Interview Practice Sessions

Preparation as per Job Description

Placement Ready Session for Working Professionals

Technical Screening for technical strengthening

Screening for effective communication check

Placement

Datadrix offers top-notch placement opportunities. With strong industry ties and modern training, we excel in placing our candidates. Our results speak to our commitment to shaping successful careers. Our approach ensures to open pathway for learners to achieve good growth in the domain

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Duration

Our 150+ hour data science course offers in-depth training and hands-on experience, covering everything from data collection to advanced analysis and visualization, preparing you to excel in the data-driven world.

Assignments

Our data science course includes assignments that offer hands-on training and cover data collection, analysis, and visualization, equipping you with essential skills for real-world professional success.

Projects

Our data science course features projects that offer practical, hands-on training in data collection, analysis, and visualization, equipping you with essential skills and real-world experience for success.

Live Classes

Our data science course includes live classes offering hands-on training and real-time guidance. From data collection to advanced analysis and visualization, you’ll gain essential skills to excel in today’s tech-driven world.

Classnotes

Our data science course includes detailed class notes that cover data collection, analysis, and visualization, providing the essential skills and practical knowledge needed to excel in the tech-driven world.

Interview Preparation

Our data science course includes targeted interview preparation, covering data collection, analysis, and visualization. This training equips you with the essential skills needed to excel in interviews and succeed in the tech-driven world.

Placements

Our data science course offers dedicated placement support, focusing on data collection, analysis, and visualization. This training equips you with the skills needed to succeed in the tech-driven world and secure your ideal job.

An Awesome Community

Our students, instructors and mentors come from different colleges, companies, and walks of life.

Meet our team & students

Joining DATADRIX means you’ll create an amazing network, make new connections, and Leverage Diverse Opportunities

“Validate Your Expertise and Propel Your Career”

  • Expand Opportunities: Certifications to unlock new career opportunities, gain credibility with employers, and open doors to higher-level positions.
  • Continuous Growth: Certifications not only validate your current skills but also encourage continuous learning and professional development, allowing you to stay updated with the latest industry trends and advancements.
  • Certification: A testament to your skills and knowledge, certifications demonstrate your proficiency in specific areas of expertise, giving you a competitive edge in the job market.
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Machine Learning

Harnessing Data’s Hidden Insights with Machine Learning

Embark on a transformative journey into the world of artificial intelligence with our Machine Learning course. Machine Learning is at the forefront of technological innovation, enabling computers to learn and adapt without explicit programming. Our comprehensive program offers a deep dive into the foundations of machine learning, from understanding algorithms and models to practical implementation. Whether you’re a seasoned data scientist or just starting your AI exploration, our course caters to all levels of expertise. Learn from leading experts in the field, gain hands-on experience with real-world datasets, and harness the power of machine learning to drive innovation and make data-driven decisions.

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Deepak Chahal
Devansh Goel
Ishty Malhotra
Taneesha Agrawal
Mayank Rana
Shubham Naithani
Kartik Sharma
Upvansh Thakur
Naveen Negi
Amita Dwivedi
Abhinav Bishnoi
Himanshu Pal
Jay Verma
Neha Sharma
Rishabh Upadhyay

Key Features & Benefits

  • Practice problems of varying difficulty
  • Over more than 5000+ Questions
  • 1:1 Expert Doubt support
  • Mock interviews with career guidance
  • 12 + Years of exprienced Faculty
  • Deep Explaination of coding
  • Practical & Project Based Learning
  • Structured feedback to make you better
  • Resume Profile Building
  • Offline / Online Modes
  • Interview Preparation
  • Production Workflow
  • Secure Certification
  • Git Github Integration
  • 24/7 Support Team
  • Projects from the scratch

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