Indian Cyber Academy

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AI & ML Data Analysis

Overview: The AI & ML Data Analysis course at Indian Cyber Academy is designed to provide students and professionals with a deep understanding of Artificial Intelligence (AI) and Machine Learning (ML) techniques, as well as their applications in data analysis. This course covers a wide range of topics, from the basics of AI and ML algorithms to the practical implementation of data analysis using these technologies. You will gain hands-on experience in building AI and ML models, analyzing datasets, and deriving actionable insights from data.

Who Should Enroll?

  • Data scientists and data analysts looking to enhance their skills
  • Aspiring AI and ML engineers
  • IT professionals interested in integrating AI/ML into their work
  • Students aiming for a career in AI, ML, and data analysis

Course Highlights:

  • Duration: 4 to 6 months (Flexible schedule)
  • Mode: Online and Classroom training
  • Level: Beginner to Intermediate
  • Certification: Industry-recognized certificate upon completion

What You Will Learn:

Module 1: Introduction to AI & ML

  1. Basics of Artificial Intelligence:
    • Overview of AI and its applications
    • Understanding key AI concepts (machine learning, deep learning, neural networks)
    • The evolution of AI and its impact on various industries
  2. Introduction to Machine Learning:
    • Supervised, unsupervised, and reinforcement learning
    • Key algorithms in ML (linear regression, decision trees, clustering)
    • The difference between AI, ML, and deep learning
  3. Mathematical Foundations for AI/ML:
    • Linear algebra, calculus, and probability theory for AI/ML
    • Data preprocessing, normalization, and feature selection
    • Data visualization techniques

Module 2: Data Collection and Preprocessing

  1. Understanding Data:
    • Types of data (structured, unstructured, semi-structured)
    • Data collection methods (APIs, web scraping, databases)
    • Cleaning and preparing data for analysis
  2. Data Preprocessing:
    • Handling missing data and outliers
    • Feature scaling and normalization
    • Encoding categorical data and dimensionality reduction (PCA, t-SNE)
  3. Exploratory Data Analysis (EDA):
    • Techniques for understanding data patterns (histograms, scatter plots, box plots)
    • Using Python libraries (Pandas, Matplotlib, Seaborn) for EDA
    • Correlation analysis and statistical testing

Module 3: Machine Learning Algorithms

  1. Supervised Learning Algorithms:
    • Regression algorithms (Linear, Polynomial, Logistic)
    • Classification algorithms (K-Nearest Neighbors, Support Vector Machines, Naive Bayes)
    • Model evaluation metrics (Accuracy, Precision, Recall, F1-score, ROC)
  2. Unsupervised Learning Algorithms:
    • Clustering algorithms (K-Means, Hierarchical Clustering)
    • Dimensionality reduction techniques (PCA, LDA)
    • Anomaly detection and association rules
  3. Reinforcement Learning:
    • Introduction to reinforcement learning concepts
    • Key algorithms (Q-Learning, Deep Q Networks)
    • Real-world applications of reinforcement learning

Module 4: Deep Learning and Neural Networks

  1. Introduction to Deep Learning:
    • Understanding neural networks and their components (neurons, layers, activation functions)
    • Basics of backpropagation and optimization techniques (Gradient Descent, Adam)
  2. Building Neural Networks:
    • Training simple neural networks with TensorFlow and Keras
    • Convolutional Neural Networks (CNNs) for image analysis
    • Recurrent Neural Networks (RNNs) and LSTMs for time-series data
  3. Transfer Learning and Fine-Tuning:
    • Using pre-trained models for faster results
    • Fine-tuning models for specific tasks (image classification, sentiment analysis)

Module 5: AI & ML in Data Analysis

  1. Model Deployment and Optimization:
    • Evaluating model performance and fine-tuning hyperparameters
    • Using Grid Search and Random Search for hyperparameter tuning
    • Deploying models using cloud platforms (AWS, Google Cloud, Azure)
  2. Data Analysis with AI/ML Models:
    • Applying machine learning models to solve real-world problems (predictive analytics, customer segmentation)
    • Data-driven decision-making and automation using AI/ML
    • Case studies in various industries (finance, healthcare, e-commerce)
  3. AI and Big Data Analytics:
    • Introduction to big data technologies (Hadoop, Spark)
    • Integrating AI/ML with big data tools
    • Analyzing large datasets with distributed systems

Module 6: Advanced Topics and Real-World Applications

  1. Natural Language Processing (NLP):
    • Text analysis, sentiment analysis, and text classification
    • Using NLP for chatbots and recommendation systems
    • Key NLP techniques (Tokenization, Lemmatization, Named Entity Recognition)
  2. AI for Computer Vision:
    • Image processing and feature extraction
    • Object detection and facial recognition
    • Image classification with CNNs
  3. AI in Business Intelligence:
    • Using AI and ML for predictive analytics in business
    • Developing recommendation systems
    • Time-series forecasting and demand prediction

Why Choose Indian Cyber Academy?

  • Expert Trainers: Learn from experienced AI and ML professionals with real-world industry knowledge.
  • Hands-on Practice: Work on live projects and datasets to gain practical experience.
  • Updated Curriculum: Stay up-to-date with the latest AI and ML techniques and tools.
  • Career Support: Job placement assistance, interview preparation, and resume building.
  • Flexible Learning: Choose from weekday, weekend, and online classes to suit your schedule.

Enroll Now: Start your journey to becoming an expert in AI & ML Data Analysis. Gain the skills to analyze complex datasets and develop intelligent solutions using AI and machine learning techniques.

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