DATA SCIENCE – ML – AI – & PowerBI Training in Bangalore

DATA SCIENCE - ML - AI - & PowerBI Training in Bangalore

ACETECK provides best DATA SCIENCE - ML - AI - & TABLEAU Training with 100% Job Placement assistance. Get trained from industry experts & Start your IT career.

Master the art of data science with our expert-led training! Learn to harness the power of data through advanced techniques in machine learning, statistical analysis, and predictive modeling. Dive into data science with hands-on experience and industry-best practices to elevate your analytical prowess and career potential.

1. Introduction to Data Science

– What is Data Science?
– Data Science vs. Data Analytics vs. Data Engineering
Applications of Data Science (Healthcare, Finance, E-commerce, etc.)
– The Data Science Process: Data Collection, Cleaning, Analysis, and Model Building

  1. Linear Algebra
    – Vectors, Matrices, and Matrix Operations
    – Eigenvalues and Eigenvectors
    – Matrix Factorization (SVD, PCA)
  2. Calculus
    – Derivatives and Gradients
    – Partial Derivatives and Chain Rule
    – Optimization Techniques (Gradient Descent)
  3. Probability and Statistics
    – Probability Theory
    – Descriptive and Inferential Statistics
    – Distributions (Normal, Binomial, Poisson)
    – Hypothesis Testing, Confidence Intervals
    – Bayesian Statistics
  1. Python for Data Science
    – Basics of Python: Data Types, Functions, Loops, Conditionals
    – Data Structures: Lists, Dictionaries, Sets, Tuples
    – Libraries: NumPy, Pandas
    – Handling Files (CSV, Excel, JSON)
  2. R for Data Science (optional)
    – Basics of R
    – Data Manipulation with dplyr
    – Data Visualization with ggplot2
  3. Version Control with Git
    – Git Basics and GitHub
  1. Pandas and NumPy for Data Manipulation
    – DataFrames, Series
    – Missing Data Handling
    – Data Transformation, Merging, Grouping
  2. Data Cleaning
    – Handling Duplicates, Outliers
    – Scaling and Normalization
    – Feature Encoding (One-Hot Encoding, Label Encoding)
  3. Working with Time Series Data
    – Resampling, Rolling Windows, Seasonal Decomposition
  1. Matplotlib and Seaborn
    – Line, Bar, Pie Charts, Histograms
    – Heatmaps, Pairplots, Boxplots
  2. Plotly and Dash for Interactive Visualizations (Optional)
  3. Tableau/Power BI (Optional)
    – Building Dashboards
    – Interactive Reports

– Univariate, Bivariate, Multivariate Analysis
 – Identifying Data Patterns and Relationships
– Handling Missing Data, Outliers
– Correlation and Covariance
– Dimensionality Reduction (PCA, t-SNE)

  1. Supervised Learning
    – Linear Regression, Logistic Regression
    – Decision Trees, Random Forests, Gradient 
    – Boosting (XGBoost, LightGBM)
    – Support Vector Machines (SVM)
    – k-Nearest Neighbors (k-NN)
  2. Unsupervised Learning
    – K-Means Clustering, Hierarchical Clustering
    – Principal Component Analysis (PCA)
    – DBSCAN, Gaussian Mixture Models
    – Ensemble Methods
    – Bagging, Boosting
    – Stacking Models
    – Model Evaluation and Validation
    – Cross-Validation, Grid Search, Random Search
    Metrics: Accuracy, Precision, Recall, F1-Score, AUC-ROC
    – Confusion Matrix, Precision-Recall Curve
  3. Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV)
  1. Introduction to Neural Networks
    – Perceptron, Activation Functions
    – Forward and Backward Propagation
  2. Deep Learning with TensorFlow/Keras
    – Building Neural Networks (Dense, CNN, RNN)
    – Convolutional Neural Networks (CNNs) for Image Data
    – Recurrent Neural Networks (RNNs) for Sequential Data
    – LSTMs, GRUs
  3. Model Optimization
    – Dropout, Batch Normalization
    – Learning Rate Scheduling, Adam Optimizer
  4. Transfer Learning
    – Pre-trained Models (VGG, ResNet, BERT)
  1. Text Preprocessing
    – Tokenization, Lemmatization, Stemming
    – Stop Words Removal, TF-IDF
  2. Text Classification
    – Sentiment Analysis, Spam Detection
  3. Topic Modeling
    – Latent Dirichlet Allocation (LDA)
  4. Word Embeddings
    – Word2Vec, GloVe
    – BERT, GPT
  1. Real-World Project (End-to-End)
    – Data Collection, Cleaning, EDA
    – Building and Evaluating Machine Learning Models
    – Model Deployment (Optional)
    – Presenting Results and Insights
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