Data Science (DS) and Machine Learning (ML) are two closely related fields that involve the extraction of valuable insights and predictions from data using advanced analytical techniques and algorithms. They play a crucial role in various industries by enabling data-driven decision-making, automation, and the development of intelligent systems. While related, Data Science and Machine Learning have distinct focuses and methodologies.
An ISO 9001:2015 &
Teacher Scientist Network Certified
Welcome to the Professional Certification Program in Data Science and Machine Learning! This comprehensive 180-day course is designed for experienced data scientists and machine learning practitioners seeking to deepen their expertise and take their skills to the next level. With over 20 years of industry and teaching experience, our expert instructors will guide you through advanced data science techniques, cutting-edge machine learning algorithms, and practical applications. By the end of this program, you will be equipped to tackle complex data challenges, build sophisticated machine learning models, and drive transformative insights for organizations.
– Master advanced data manipulation, exploration, and feature engineering techniques.
– Develop expertise in advanced machine learning algorithms and deep learning.
– Explore big data technologies and distributed computing for large-scale data analysis.
– Apply your knowledge through real-world projects and case studies.
– Leverage data storytelling and visualization for impactful communication.
– Advanced data cleaning and preprocessing techniques
– Handling missing data and outliers effectively
– Feature engineering and selection for complex datasets
– Dealing with imbalanced data and rare events -Time series analysis and forecasting
– Ensemble learning methods: Random Forests, Gradient Boosting, Stacking, etc.
– Support Vector Machines (SVM) and kernel methods
– Neural Networks Deep Learning architectures, transfer learning, and fine-tuning
– Unsupervised learning techniques: Clustering, Dimensionality Reduction, and Autoencoders
– Reinforcement Learning: Basics and advanced algorithms
– Advanced evaluation metrics for classification, regression, and clustering tasks
– Cross-validation strategies for reliable model performance assessment
– Hyperparameter tuning using Grid Search, Random Search, and Bayesian Optimization
– Bias-variance trade-off and model generalization
– Introduction to distributed systems: Apache Hadoop, Spark, and Dask
– Scaling machine learning algorithms for big datasets
– Parallel processing and optimization techniques.
– Cloud-based solutions for data processing and machine learning.
– Text data preprocessing and tokenization
– Word embeddings: Word2Vec, Glove, and FastText – Advanced NLP techniques: Named Entity Recognition, Sentiment Analysis, and Language Generation
– Deep learning models for NLP: Recurrent Neural Networks (RNNs) and Transformer-based architectures
– Design and evaluation of accounting information systems.
– Enterprise resource planning (ERP) systems.
– Cybersecurity and data privacy concerns.
– ARIMA (AutoRegressive Integrated Moving Average) models
– Seasonal decomposition of time series
– Exponential smoothing methods
– Long Short-Term Memory (LSTM) networks for time series forecasting
Title: Stock Market Prediction with Time Series Analysis
In this project, you will build a sophisticated stock market prediction system using time series analysis and machine learning. The goal is to predict stock prices for a specific company based on historical stock data. The dataset will contain daily stock price information, volume, and other relevant features.
1. Data Preprocessing:
Clean the dataset, handle missing values, and create appropriate features.
2. Time Series Analysis:
Perform exploratory data analysis to understand trends, seasonality, and other patterns.
3. Feature Engineering:
Extract relevant features and lagged variables for modeling.
4. Model Selection:
Compare different time series forecasting models (e.g., ARIMA, Exponential Smoothing, LSTM) to identify the best-performing one.
5. Model Training:
Split the dataset into training and testing sets, train the selected model(s), and tune hyperparameters.
6. Model Evaluation:
Evaluate the model's performance using appropriate metrics and visualize the predictions against actual stock prices.
7. Future Price Prediction:
Use the trained model to predict future stock prices and assess its accuracy.
Course Essentials :
This program is tailored for experienced data scientists, machine leaming engineers, and professionals looking to enhance their expertise in advanced data science techniques. It is also suitable for individuals with prior experience in data science and machine learning who want to stay updated with the latest advancements in the field.
Earned with Excellence: