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Unlock data insights with Python-powered machine learning

Master data science and machine learning with Python, gaining expertise in data analysis, predictive modeling, and real-world applications
  • Course Duration : 3-6 Months
  • Certification Guaranteed
  • Job Oriented Training
  • 100% Placement Opportunities Guaranteed
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Certification in Data Science & Machine Learning with Python

Data Science & Machine Learning with Python is a specialized Droit Academy course designed to teach individuals how to analyze data, build machine learning models, and apply Python programming for data-driven decision-making. Python is one of the most widely used languages in data science and machine learning due to its simplicity and a rich ecosystem of libraries and tools.

Topics Covered in a Data Science & Machine Learning with Python Course

  • Introduction to Data Science and Machine Learning
  • Basics of data science, its applications, and the importance of machine learning in data analysis.
  • Overview of the machine learning pipeline: data collection, preprocessing, feature engineering, modeling, evaluation, and deployment.
  • Python Programming for Data Science
  • Python fundamentals: data types, functions, loops, and libraries.
  • Key Python libraries for data science:-
    NumPy: For numerical operations.
    Pandas: For data manipulation and analysis.
    Matplotlib and Seaborn: For data visualization.
    Scikit-learn: For machine learning algorithms.
  • Data Preprocessing
  • Understanding how to clean and preprocess data before analysis.
  • Techniques for handling missing values, data normalization, encoding categorical variables, and scaling features.
  • Data wrangling and manipulation using Pandas.
  • Exploratory Data Analysis (EDA)
  • Visualizing and analyzing data to understand its underlying patterns and structure. .
  • Summary statistics, correlation analysis, and creating visualizations to explore data distributions, trends, and relationships
  • Supervised Learning Algorithms
  • Regression Models: Linear regression, polynomial regression, and support vector regression.
  • Classification Models: Logistic regression, k-Nearest Neighbors (KNN), Decision Trees, Random Forest, and Support Vector Machines (SVM).
  • Model evaluation techniques: accuracy, precision, recall, F1 score, confusion matrix.
  • Unsupervised Learning Algorithms
  • Clustering Algorithms: K-Means, Hierarchical Clustering, DBSCAN.
  • Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE for visualizing high-dimensional data.
  • Advanced Machine Learning Models
  • Ensemble Methods: Random Forest, Gradient Boosting (XGBoost, LightGBM), and AdaBoost.
  • Neural Networks and Deep Learning: Introduction to deep learning, basics of neural networks, and frameworks like TensorFlow and Keras.
  • Model Evaluation and Optimization
  • Techniques like cross-validation, hyperparameter tuning (e.g., Grid Search, Random Search), and model selection.
  • Addressing overfitting and underfitting using regularization (L1, L2).
  • Natural Language Processing (NLP) with Python
  • Text analysis and processing: tokenization, stemming, lemmatization, stop word removal.
  • Working with NLTK, spaCy, and TextBlob for text classification, sentiment analysis, and topic modeling.
  • Time Series Analysis and Forecasting
  • Techniques for working with time-series data: trend analysis, seasonal decomposition.
  • Forecasting methods using ARIMA, Exponential Smoothing, and machine learning models.
  • Data Science Projects and Case Studies
  • Working on real-world projects using datasets from various domains (finance, healthcare, marketing, etc.).
  • Applying machine learning models and presenting results using data visualization tools

Entry Requirements

  • Basic knowledge of Python programming is helpful but not mandatory for all courses.
  • Some institutions may require familiarity with statistics and algebra, while others offer beginner-friendly tracks.

Tools and Libraries Covered

  • Python Libraries : Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, PyTorch, NLTK, spaCy.
  • Data Visualization Tools :Tableau (optional), Power BI (optional), Matplotlib, and Seaborn for Python-based visualizations.
  • Jupyter Notebooks or Google Colab : For writing and executing Python code interactively.

Career Opportunity

  • Python Programming Language
  • Jupyter Notebooks for Coding and Experiments
  • Scikit - Learn for Machin Learning
  • TensorFlow and PyTorch for Deep Learning
  • Keras for Neural Network Building
  • Open CV for Computer Vision Tasks
  • NLTK and spaCY for Natural Language Processing

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