Glomacs Institute

Our Data Science Track 2 builds on the foundational concepts in our AI/DS/Analytics Track 1 and offers a more in-depth, deep dive into the domain of Advanced Analytics, Machine Learning and Artificial Intelligence. Our DS Track 2 presents an overview of machine learning concepts, algorithms, methodologies, and processes. Offering introduction into the fundamental concept of deep learning with practical illustration of how deep learning is being applied in practice. This track will enable you to work on Data Science and Advanced Analytics projects with data from real-life, practical use-cases across different industries and sectors. You will be able to develop machine learning models using supervised and unsupervised learning algorithms such as regression, classification, clustering, and time series forecasting. You will also be exposed to two specialization in Artificial Intelligence: Computer vision for object detection, image / video analysis and text analytics using Natural language processing.

Note: Registration for TRACK 2 include taking the modules in TRACK 1. Making Track 2 a total of 20 weeks.

Understand real-life Machine Learning and AI use-cases through end-to-end projects and over 20+ hands-on exercises across different domains

Understand basic concepts of Computer Vision for Image/video content analysis and Natural Language Processing for Text Analytics

Gain basic knowledge of Deep Learning Concepts such as Feed-Forward Neural Networks, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) and practical implementation in real-life cases using tools such as Theano, Tensorflow and Keras

An industry proven template to help you with your foot in the door

Don’t you love to have direct access with your instructor, guidance and mentor-ship when you need it

Access to datasets, case studies to build your portfolio, and capstone projects to help you stay rooted.

Full Payment

Two Installments Payment

Three Installments Payment

Module 1: Introduction

- Track 2 Content Introduction

Module 2: The Machine Learning (ML) Landscape

- Learning Objectives
- What is Machine Learning and Why Machine Learning?
- Relationship Between Artificial Intelligence, Machine Learning, Deep Learning and Data Science
- Examples of AI, ML, DL Applications – Real Life Use-Cases
- How Machines Learn
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning

- Main Challenges of ML
- Essential Python Libraries and Tools for Machine Learning and Deep Learning

Module 3: Machine Learning Project Steps

- Look at the Big Picture and Frame the Problem
- Getting the Data
- Explore and Visualize the Data to gain Insight
- Prepare the Data for ML Algorithms
- Data Cleaning (Outliers, Missing Data)
- Handling Text and Categorical Variables
- Feature Selection
- Feature Scaling (Normalization / Standardization)
- Feature Transformation

- Select and Train a Model
- Use of Cross-Validation

- Fine-tune your model
- Generate Report or Launch your model in production

Module 4: Supervised Learning: Classification

- Learning Objectives
- Types of Classification Algorithms
- Training a Classifier
- Classification Algorithms:
- K-Nearest Neighbours
- Decision Trees
- Building Decision Trees

- Logistic Regression
- Logistic regression vs Linear regression
- Logistic Regression Training13m

- Support Vector Machine8m

- Performance Measures
- Measuring Accuracy Using Cross-Validation
- Confusion Matrix
- Precision and Recall
- Precision/Recall trade-off
- The ROC Curve

- Error Analysis

Module 5 - Supervised Learning: Regression

- Learning Objectives
- Regression Algorithms:
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression

- Regularized Linear Models
- Ridge Regression
- Lasso Regression

- Hands-on Examples and Implementation for each algorithm
- Performance Measures
- Mean Absolute Error (MAE)
- Mean Square Error (MSE)
- R
^{2}

Module 6 - Unsupervised Learning

- Learning Objectives
- Segmentation and Clustering Concepts
**Clustering Algorithms:**- K-Means
- Hierarchical Clustering

- Evaluating Clustering performance
**Dimensionality Reduction:**- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Kernel PCA

**Association Rule Learning:**- APRIORI (Market basket analysis for Recommender Systems)

**Reinforcement Learning:**- Thompson Sampling

- Real-Life Hands-On Project on each algorithm
- Segmentation with Decision Trees

Module 7 - Time Series Forecasting with ARIMA

- Learning Objectives
- What is Time Series Forecasting?
- Time Series Nomenclature
- Components of Time Series – Levels, Trends, Seasonality, Noise
- Concerns of Forecasting
- End to End Time Series Forecasting using ARIMA: Stock Market Price Forecast
- Key takeaways

Module 8 – Natural Language Processing

- Learning Objectives
- Introduction to NLP and Real-life Use Cases
- Building Blocks of Human Language
- Common NLP Tasks and Why it is Challenging
- Exact and Fuzzy Text Matching
- Introduction to NLP packages: SpaCy and NLTK
- Fundamentals of Regular Expression (RE)
- Extracting Information from PDF (pdfextract)
- Sentence Detection
- Tokenization
- Stop Word Removal
- N-grams
- Stemming and Lemmatization
- POS Tagging
- Word Frequency
- Named Entity Recognition
- Rule-based Matching (Token, Phrase, Entity Ruler)
- Sentiment Analysis and Practical Implementation
- Key Takeaways

Module 9 – Computer Vision

- Learning Objectives
- Overview of Computer Vision and Image Analysis
- Examples of Real-Life Applications of Text Analysis
- Overview of OpenCV toolkit
- Handling Images and Video Files
- Reading and Writing Images
- Capturing and Saving Videos
- Processing and Enhancing Images (Filtering, Sharpening, Denoising etc)
- Procession Colors (Color Spaces, Color-space-based segmentation, Color transfer)
- Performing Feature Detection
- Detecting Specific Objects such as faces, eyes, cars, in videos or images
- Analyzing video (estimating the motion in it, subtract background, and track objects in it)
- Key Takeaways

Module 10 – Introduction to Deep Learning

- Learning Objectives
- What is Deep Learning
- The Neuron
- The Activation Function
- A Simple Perceptron
- How does Neural Network Work?
- How does Neural Network Learn?
- Feed-Forward Neural Network
- Gradient Descent and Stochastic Gradient Descent
- Backpropagation
- Step-by-Step how to build an Artificial Neural Network
- Convolution Neural Network (CNN)
- Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)
- Hands-on Deep Learning Projects
- Key Takeaways

When is the Data Science Track 2 starting?

Data Science Track 2 is built on Data Science track 1 and would start immediately after track 1 finishes. Track 1 Class Starts **Saturday, March 04, 2023**

What is the deadline for registration?

Registration for the next batch of track 1 on which track 2 is built is due on March 01, 2023.

Where and when do the classes take place?

The class is an interactive, online, instructor-led training for a total of 24 weeks (track 1 &2). Classes take place on Saturdays.

How much is the registration cost for the course?

The discounted registration cost for our combined DS track 1 and 2 is CAD $5,000. For those who register on or before Feb 20, 2023, you get an early bird discounted price of $4,600. To help as many people get into the course, we also have 2-3 installment payment plan options.

Where do I get further information about the class

For further information about the class, send an email to: training@glomacssolutions.com

Where do I get further information about the class

For further enquiries about the class, send an email to: training@glomacssolutions.com

Taking our Data Science Track 2 will put you at the forefront of Artificial Intelligence and Data Science. The most on demand jobs globally