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Artificial Intelligence and
Machine Learning

Master AI and ML to turn data into insightful knowledge that will spur innovation and influence the future.

Duration: 45 Days

  • 100% Practical
  • Certification
  • 7+ Project Assignments
  • Agency Style Training

Real-World Applications

In-Demand Skills

Expert Guidance

Certification

Learning Curriculum

  1. Getting Started with Python

1.1 Python Introduction and Environment Setup

  • Introduction to Python programming language.
  • Installing Python and setting up IDEs (e.g., PyCharm, VS Code, Jupyter Notebooks).
  • Overview of Python’s applications in data science and machine learning.

1.2 Python Basic Syntax and Data Types

  • Variables, constants, and comments.
  • Data types: int, float, bool, string, and complex.
  • Typecasting between different data types.

1.3 Operators in Python

  • Arithmetic, comparison, logical, bitwise, assignment, and identity operators.
  • Operator precedence and associativity.

1.4 Working with Strings

  • Creating, indexing, slicing, and manipulating strings.
  • String methods: split(), join(), replace(), upper(), lower(), etc.

1.5 Python Collections

  • Lists: Creating, accessing, updating, and manipulating lists.
  • Tuples: Characteristics of tuples, tuple operations, immutability.
  • Sets: Operations on sets (union, intersection, difference), set methods.
  • Dictionaries: Key-value pairs, dictionary methods, nested dictionaries.

1.6 Conditional Statements

  • if, if-else, and if-elif-else statements.
  • Nested conditions, and best practices for readability.

1.7 Loops in Python

  • for and while loops.
  • Using loop controls (break, continue, pass).
  • Looping through collections and custom ranges.

1.8 List and Dictionary Comprehension

  • Simplifying loops with list and dictionary comprehensions.
  • Applications in data manipulation and generation.

1.9 Functions in Python

  • Defining and calling functions.
  • Arguments, keyword arguments, and default parameters.
  • Returning values from functions.

1.10 Anonymous Functions (Lambda Expressions)

  • Writing and using lambda functions.
  • Using lambda with map(), filter(), and reduce().

1.11 Generators

  • Introduction to generators and yield keyword.
  • Differences between generators and iterators.
  • Use cases for generators in memory-efficient programming.

1.12 Modules in Python

  • Creating and importing modules.
  • Using built-in modules (e.g., math, random, os).
  • Working with third-party libraries (pip and package management).

1.13 Exceptions and Error Handling

  • try, except, else, and finally blocks.
  • Raising exceptions (raise), custom exceptions.

1.14 Object-Oriented Programming (OOP) in Python

  • Classes and Objects: Creating classes, instantiating objects.
  • Methods: Instance methods, class methods, static methods.
  • Inheritance: Single, multiple inheritance, method overriding.
  • Polymorphism: Method overloading and overriding.
  • Operator Overloading: Overloading operators for custom object behavior.

1.15 Working with Dates and Times

  • Introduction to datetime module.
  • Parsing, formatting, and manipulating dates and times.
  • Timezone handling and time arithmetic.

1.16 Regular Expressions (Regex)

  • Introduction to regular expressions.
  • Functions: re.search(), re.match(), re.compile(), re.split().
  • Pattern matching, substitution, and validation.

1.17 File Handling

  • Opening, reading, writing, and closing files in Python.
  • Reading from and writing to CSV and JSON files.
  • File pointers, modes (read, write, append), and exception handling in file operations.

1.18 APIs: The Unsung Hero of the Connected World

  • Introduction to APIs, RESTful APIs.
  • Using Python’s requests module to interact with APIs.
  • Authentication, GET and POST requests, and handling API responses.

1.19 Python for Web Development: Flask

  • Introduction to Flask web framework.
  • Setting up a basic Flask project.
  • Building web routes, rendering HTML templates.
  • Connecting a Python app to a database (SQLite, MySQL).

1.20 Hands-On Python Projects

  • Web Scraping: Using BeautifulSoup and requests to scrape data from websites.
  • Sending Automated Emails: Using Python to send automated emails with attachments.
  • Building a Virtual Assistant: Voice recognition, task automation, and response generation using Python.

Mathematics

2.1 Introduction to Statistics and Probability

  • Descriptive statistics: mean, median, mode, variance, standard deviation.
  • Probability concepts: conditional probability, Bayes’ theorem, probability distributions.

2.2 Linear Algebra

  • Vectors, matrices, and matrix operations.
  • Eigenvalues and eigenvectors.
  • Dot product, cross product, and matrix factorization.

2.3 Calculus for Machine Learning

  • Derivatives and integrals.
  • Partial derivatives, gradients, and optimization.

Understanding cost functions and gradient descent.

Data Analysis and Visualization Libraries

3.1 Numpy

  • Working with arrays, array operations, and broadcasting.
  • Linear algebra with Numpy.
  • Random number generation and simulations.

3.2 Pandas

  • DataFrames, Series, and Indexes.
  • Reading from and writing to CSV, Excel, and databases.
  • Data manipulation: filtering, grouping, aggregating, merging, and reshaping.

3.3 Matplotlib and Seaborn

  • Creating visualizations: line plots, scatter plots, bar charts, histograms.
  • Customizing plots (titles, labels, legends).
  • Using Seaborn for advanced plots (heatmaps, pair plots, and box plots).

3.4 Web Scraping

  • Ethical considerations in web scraping.
  • Tools and libraries: BeautifulSoup, Selenium, Scrapy.
  • Storing and processing scraped data.

3.5 Exploratory Data Analysis (EDA)

  • Techniques for exploring datasets.
  • Identifying missing data, outliers, and data patterns.
  • Visualizing distributions, correlations, and feature relationships.

3.6 Database Access and SQL

  • SQL basics: SELECT, INSERT, UPDATE, DELETE.
  • Joining tables, aggregations, and filtering data.
  • Connecting Python to databases (e.g., SQLite, MySQL).

3.7 Power BI

  • Introduction to Power BI desktop.
  • Connecting to various data sources.
  • Creating interactive dashboards and reports.

Data Manipulation and Preprocessing

4.1 Data Cleaning and Transformation

  • Handling missing data: imputation, dropping.
  • Dealing with duplicates, inconsistent data formats, and incorrect entries.

4.2 Feature Engineering

  • Creating new features from existing data.
  • Interaction terms, binning, encoding categorical variables (one-hot encoding).

4.3 Feature Scaling and Normalization

  • Standardization vs normalization.
  • Techniques: MinMaxScaler, StandardScaler.

4.4 Handling Categorical Variables

  • Encoding categorical variables: LabelEncoder, OneHotEncoder.
  • Dealing with high cardinality categorical variables.

Machine Learning

5.1 Supervised Learning

  • Regression Models:
    • Simple Linear Regression, Polynomial Regression, Ridge, Lasso Regression.
    • Practical applications and implementation using scikit-learn.
  • Classification Models:
    • Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM).
    • Performance metrics: Confusion matrix, ROC curve, AUC, Precision, Recall, F1-score.

5.2 Unsupervised Learning

  • Clustering:
    • K-Means Clustering, Hierarchical Clustering, DBSCAN.
    • Visualizing clusters and analyzing cluster results.
  • Dimensionality Reduction:
    • Principal Component Analysis (PCA), t-SNE for data visualization.

5.3 Model Evaluation and Validation

  • Train-test split, cross-validation.
  • Hyperparameter tuning: GridSearchCV, RandomizedSearchCV.

Overfitting, underfitting, and model selection.

Deep Learning

6.1 Artificial Neural Networks (ANN)

  • Structure of neural networks: neurons, layers, activation functions.
  • Forward propagation, backpropagation, and gradient descent.

6.2 Convolutional Neural Networks (CNN)

  • CNN architecture: Convolution layers, pooling layers, fully connected layers.
  • Applications of CNNs: Image classification, object detection.
  • Hands-on: Implementing CNNs using TensorFlow/Keras

6.3 Recurrent Neural Networks (RNN)

  • Introduction to sequence data.
  • Understanding vanishing gradients and the role of LSTMs and GRUs.
  • Applications: Time-series forecasting, text generation.

6.4 Long Short-Term Memory (LSTM) Networks

  • Understanding LSTM architecture.
  • Use cases: Sentiment analysis, sequence prediction, speech recognition.

6.5 Generative Adversarial Networks (GANs)

    • GAN architecture: Generator and discriminator networks.
    • Training GANs: Understanding adversarial training and loss functions.
    • Applications of GANs: Image generation, style transfer, synthetic data creation.

Natural Language Processing (NLP)

7.1 Text Preprocessing

  • Tokenization, stopword removal, stemming, and lemmatization.
  • Sentence splitting and named entity recognition (NER).

7.2 Text Representation

  • Bag of Words, TF-IDF, and word embeddings.
  • Word2Vec and GloVe embeddings for semantic analysis.

7.3 NLP Models

  • Sentiment analysis, topic modeling, and text classification.
  • Sequence-to-sequence models for language translation, summarization, and chatbot creation.

7.4 Transformer Models

  • Introduction to transformer architectures.
  • Understanding the attention mechanism.
  • Pre-trained transformer models: BERT, GPT, T5.
  • Fine-tuning transformer models for downstream tasks.

7.5 Large Language Models (LLMs)

  • Overview of LLMs: GPT-3, GPT-4, BERT.
  • Understanding zero-shot, few-shot, and fine-tuning capabilities of LLMs.
  • Practical implementation: Text generation, summarization, and conversational AI.

Transfer Learning

8.1 Introduction to Transfer Learning

  • Concept of transfer learning and pre-trained models.
  • Applications in computer vision and NLP.

8.2 Pretrained Models for Image and Text

  • Pretrained models in computer vision: VGG16, ResNet, Inception.
  • Pretrained models in NLP: BERT, GPT, XLNet.
  • Fine-tuning pretrained models for custom tasks.

8.3 Hands-On: Using Transfer Learning

  • Implementing transfer learning in image classification using Keras/TensorFlow.
  • Fine-tuning a pre-trained NLP model for text classification tasks.

Time Series

9.1 Introduction to Time Series Analysis

  • Understanding time series data, decomposition into trend, seasonality, and noise.

9.2 Statistical Methods for Time Series

  • Descriptive statistics: Mean, variance, autocorrelation.
  • Smoothing techniques: Moving averages, exponential smoothing.

9.3 Stationarity and Transformation

  • Testing for stationarity: ADF Test, KPSS Test.
  • Differencing and log transformation.

9.4 ARIMA Models

  • Building AR, MA, and ARIMA models for forecasting.
  • Seasonal ARIMA (SARIMA) for handling seasonality.

9.5 Machine Learning Approaches

  • Random Forest, Gradient Boosting for time series forecasting.
  • Using RNN and LSTM for time series data.

9.6 Time Series Forecasting Libraries

  • Python libraries for time series forecasting: statsmodels, Prophet, pmdarima.

9.7 Model Evaluation

  • Forecast accuracy metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE).

. Advanced Deep Learning

10.1 Transfer Learning

  • Using transfer learning for deep learning tasks in both computer vision and NLP.

10.2 Generative Adversarial Networks (GANs)

  • Deep dive into advanced GAN architectures and applications.

10.3 NLP with Large Language Models (LLMs)

  • Fine-tuning LLMs for custom tasks: text generation, summarization, translation.

10.4 Hands-On Projects

  • Building real-world projects using Transfer Learning, GANs, and LLMs.

Project Work

11.1 Project Overview

  • Objective: To apply the concepts, techniques, and tools learned throughout the course to real-world data science projects.
  • Approach: Each project will include a problem statement, data collection, exploratory data analysis, modeling, and presentation of results.

11.2 Project Phases

  1. Project Selection
    • Identify a real-world problem or opportunity in data science.
    • Develop a clear problem statement and objectives for the project.
  2. Data Collection
    • Gather relevant datasets from various sources (public datasets, APIs, web scraping).
    • Ensure data quality and appropriateness for the problem.
  3. Exploratory Data Analysis (EDA)
    • Conduct EDA to understand data distributions, relationships, and patterns.
    • Use visualizations to communicate findings and insights.
  4. Data Preprocessing
    • Clean and preprocess the data (handling missing values, outlier detection, feature engineering).
    • Perform necessary transformations (scaling, encoding categorical variables).
  5. Model Development
    • Choose appropriate models based on the problem type (supervised, unsupervised, time series).
    • Implement multiple models and evaluate their performance using suitable metrics.
    • Optimize model parameters through techniques like grid search or random search.
  6. Model Evaluation and Selection
    • Compare models based on performance metrics (accuracy, precision, recall, F1-score).
    • Select the best-performing model for deployment.
  7. Deployment and Presentation
    • Deploy the model using a simple web application (e.g., Flask) or notebook interface.
    • Create a comprehensive report summarizing the project, methodologies used, challenges faced, and key findings.
    • Prepare a presentation to share results with peers, focusing on storytelling and visualization techniques.

11.3 Types of Projects

  • Data Analysis Project: Analyze a dataset to derive insights and visualizations (e.g., customer segmentation, sales analysis).
  • Predictive Modeling Project: Build a predictive model to forecast outcomes (e.g., housing price prediction, churn prediction).
  • Time Series Project: Analyze and forecast time series data (e.g., stock prices, sales forecasting).
  • NLP Project: Create a model for text classification, sentiment analysis, or language generation (e.g., news categorization, chatbot).
  • Computer Vision Project: Develop an image classification or object detection model (e.g., identifying plant species, facial recognition).
  • Generative Models Project: Use GANs to create synthetic data or images (e.g., generating artwork or realistic images).

11.4 Final Showcase

  • Conduct a final showcase where students present their projects to the class.
  • Engage in Q&A sessions to discuss methodologies, results, and potential improvements.
  • Encourage peer feedback and collaboration on project ideas.
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