Machine Learning Course in Hyderabad

• Introduction to Machine Learning and its types (supervised, unsupervised, reinforcement learning)
• Setting up the development environment (Python, Jupyter Notebook, libraries: NumPy, Pandas, Scikit-learn)
• Overview of the Machine Learning workflow and common data preprocessing techniques

• Definition of data science and its role in various industries.
• Explanation of the data science lifecycle and its key stages.
• Overview of the different types of data: structured, unstructured, and semi-structured.
• Discussion of the importance of data collection, data quality, and data preprocessing.

• Introduction to Pandas, a Python library for data manipulation and analysis.
• Overview of NumPy, a fundamental package for scientific computing with Python.
• Explanation of key data structures in Pandas: Series and Data Frame.
• Hands-on exploration of data using Pandas to summarize, filter, and transform data.
• Data cleaning techniques, handling missing values, and dealing with outliers.
• Statistical analysis of data using NumPy functions.

• Introduction to data visualization and its importance in data analysis.
• Overview of Matplotlib, a popular plotting library in Python.
• Exploring different types of plots: line plots, scatter plots, bar plots, histogram, etc.
• Customizing plots with labels, titles, colors, and styles.
• Introduction to Seaborn, a Python data visualization library based on Matplotlib.
• Advanced plotting techniques with Seaborn: heatmaps, pair plots, and categorical plots.
• Introduction to Plotly, an interactive plotting library for creating web-based visualizations.
• Creating interactive and dynamic visualizations with Plotly

Hands-on: Instagram Reach Analysis

• Introduction to Data Engineering: Data cleaning, transformation, and integration
• Data cleaning and Handling missing values: Imputation, deletion, and outlier treatment
• Feature Engineering techniques: Creating new features, handling date and time variables, and encoding categorical variables
• Data Scaling and Normalization: Standardization, min-max scaling, etc.
• Dealing with categorical variables: One-hot encoding, label encoding, etc.

• Introduction to web scraping: Tools, libraries, and ethical considerations
• Scraping data from websites using libraries like BeautifulSoup and requests: HTML
  parsing, locating elements, and extracting data
• Handling different types of data on websites: Tables, forms, etc.
• Storing scraped data in appropriate formats: CSV, JSON, or databases

Hands-on: Working on Scraping Data from Static / Dynamic Website

• Introduction to Regression: Definition, types, and use cases
• Linear Regression: Theory, cost function, gradient descent, and assumptions
• Polynomial Regression: Adding polynomial terms, degree selection, and overfitting
• Lasso and Ridge Regression: Regularization techniques for controlling model complexity
• Evaluation metrics for regression models: Mean Squared Error (MSE), R-squared, and Mean Absolute Error (MAE)
  
  

Hands-On – Video Game Sales Prediction

• Introduction to Classification: Definition, types, and use cases
• Logistic Regression: Theory, logistic function, binary and multiclass classification
• Decision Trees: Construction, splitting criteria, pruning, and visualization
• Random Forests: Ensemble learning, bagging, and feature importance
• Evaluation metrics for classification models: Accuracy, Precision, Recall, F1-score, and ROC curves
• Implementation of classification models using scikit-learn library

Hands-On – Heart Disease Detection & Food Order Prediction

• Support Vector Machines (SVM): Study SVM theory, different kernel functions (linear, polynomial, radial basis function), and the margin concept. Implement SVM classification and regression, and evaluate the models.
• K-Nearest Neighbors (KNN): Understand the KNN algorithm, distance metrics, and the concept of K in KNN. Implement KNN classification and regression, and evaluate the models.
• Naive Bayes: Learn about the Naive Bayes algorithm, conditional probability, and Bayes’ theorem. Implement Naive Bayes classification, and evaluate the model’s performance.

Hands-On – Contact Tracing & Sarcasm Detection

• AdaBoost: Boosting technique, weak learners, and iterative weight adjustment
• Gradient Boosting (XGBoost): Gradient boosting algorithm, Regularization, and hyperparameter tuning
• Evaluation and fine-tuning of ensemble models: Cross-validation, grid search, and model selection
• Handling imbalanced datasets: Techniques for dealing with class imbalance, such as oversampling and undersampling

Hands-On – Medical Insurance Price Prediction

• Introduction to Clustering: Definition, types, and use cases
• K-means clustering: Algorithm steps, initialization methods, and elbow method for determining the number of clusters
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Core
  points, density reachability, and epsilon-neighborhoods
• Evaluation of clustering algorithms: Silhouette score, cohesion, and separation metrics

Hands-On – Credit Card Clustering

• Introduction to Dimensionality Reduction: Curse of dimensionality, feature
  extraction, and feature selection
• Principal Component Analysis (PCA): Eigenvectors, eigenvalues, variance explained, and dimensionality reduction
• Implementation of PCA using scikit-learn library

Hands-On – MNIST Data

• Cross-validation and model evaluation techniques
• Hyperparameter tuning using GridSearchCV and RandomizedSearchCV
• Model selection and comparison

• Introduction to NLP: Understand the basics of NLP, its applications, and challenges.
• Text Preprocessing: Learn about tokenization, stemming, lemmatization, stop word removal, and other techniques for text preprocessing.
• Text Representation: Explore techniques such as Bag-of-Words (BOW), TF-IDF, and
  word embeddings (e.g., Word2Vec, Glove) for representing text data.
• Sentiment Analysis: Study sentiment analysis techniques, build a sentiment analysis model using supervised learning, and evaluate its performance.

Hands-On – Real Time Sentiment Analysis

• Introduction to Recommendation Systems: Understand the concept of
  recommendation systems, different types (collaborative filtering, content-based, hybrid), and evaluation metrics.
• Collaborative Filtering: Explore collaborative filtering techniques, including
  user-based and item-based approaches, and implement a collaborative filtering model.
• Content-Based Filtering: Study content-based filtering methods, such as TF-IDF and cosine similarity, and build a content-based recommendation system.
• Deployment and Future Directions: Discuss the deployment of recommendation systems and explore advanced topics in NLP and recommendation systems.

Hands-On – News Recommendation System

• Introduction to Reinforcement Learning: Agent, environment, state, action, and reward
• Markov Decision Processes (MDP): Markov property, transition probabilities, and value functions
• Q-Learning algorithm: Exploration vs. exploitation, Q-table, and learning rate
• Hands-on reinforcement learning projects and exercises

Hands-On – Working with OpenAl Gym

• Introduction to Flask / Stream lit web framework
• Creating a Flask/Streamlit application for ML model deployment
• Integrating data preprocessing and ML model
• Designing a user-friendly web interface

• Building a web application for Machine Learning models: Creating forms, handling user input, and displaying results
• Deployment using AWS (Amazon Web Services): Setting up an AWS instance, configuring security groups, and deploying the application
• Deployment using PythonAnywhere: Uploading Flask application files, configuring WSGI, and launching the application
  
  

• Work on a real-world Machine Learning project: Identify a problem, gather data,
  and define project scope
• Apply the learned concepts and algorithms: Data collection, preprocessing, model building, and evaluation
• Deployment of the project on AWS or PythonAnywhere: Showcase the developed application and share the project with others
• Presentation and discussion of the project: Demonstrate the project, explain design decisions, and receive feedback