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Machine Learning Course

Course Detail

Machine Learning

Machine Learning

Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics.This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning . You will follow along and code your own projects using some of the most relevant open source frameworks and libraries and you will apply what you have learned in various courses by completing a final capstone project.Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics.In addition to earning a Professional Certificate from Oxford Global Academy of Excellence, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning

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Module 1
Cohort Orientation:
  • A brief introduction to tools related to data
  • Learn about particular real-time projects and Capstone projects
  • Data and its impact on career opportunities
  • Utilizing data, to enhance industrial operations and management
Fundamentals of Programming:
  • Introduction to Anaconda & Jupyter notebook
  • Flavors of python Introduction to Git, GitHub
  • Python Fundamentals
Fundamentals of Statistics:
  • Mean, Median, Mode
  • Standard Deviation, Average. Probability, Permutations, and Combinations
  • Introduction to Linear Algebra
Module 2
Python Programming:
  • Programming Basics & Environment Setup
  • Python Programming Overview
  • Strings, Decisions & Loop Control
  • Python Data Types
  • Functions And Modules
  • Class hands-on: 8+ Programs to be covered in the functions, Lambda, modules Generators, and Packages class.
  • Data Analysis Using Numpy
  • Data Analysis Using Pandas
  • Data Visualization using Matplotlib
  • Data Visualization using Seaborn
  • Case Study on Numpy, Pandas, Matplotlib 1 Case Study on Pandas and Seaborn
Module 3
  • Fundamentals of Math and Probability
  • All about Population & Sample
  • Introduction to Statistics, Statistical Thinking
  • Descriptive Statistics
  • Inferential Statistics
  • Hypothesis Testing
  • Linear Algebra
  • Data Processing & Exploratory Data Analysis
  • EDA
  • Statistics Assignments: Total 4 practice sets and Assignments from Statistics
Machine Learning:
  • Introduction to Machine Learning
  • Regression and Classification Models
  • Linear Regression Model
  • Data Preprocessing
  • Encoding the Data
  • Logistic Regression Model
  • Evaluation Metrics for Classification model
  • K Nearest Neighbours Model
  • Decision Tree Model
  • Random Forest Model
  • Hyperparameter Tuning
  • Naive Baye’s Model
  • Case Study on Kart Model Business & Random Forest
  • K Means and Hierarchical Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA):
  • Support Vector Machine (SVM)
  • Case Study on Recommendation Engine for e-commerce/retail chain & Twitter data analysis using NLP
Module 4
  • SQL and RDBMS
  • Advance SQL
  • NoSQL, HBase & MongoDB
  • JSON Data & CRUD
  • Programming with SQL
  • Introduction to MongoDB
  • MongoDB (Advance)
  • Introduction to Tableau
  • Visual Analytics
  • Dashboard and Stories
  • Tableau (Advance)
  • Hands-on: Connecting data source and data cleansing
  • Working with various charts, Deployment of Predictive model in visualization
  • Getting Started With Power BI
  • Programming with Power BI
Big Data & Spark Analytics:
  • Introduction To Hadoop & Big Data
  • What is Spark
  • Getting to know PySpark
  • Hands-on: Map reduce Use Case: Youtube data analysis & Spark RDD programming
Time Series:
  • Introduction to Time Series Forecasting
  • Introduction to ARIMA Models
  • Regex pattern and its interpretation.
  • Case Study on Time series classification of smartphone data to predict user behavior
  • Performing Time Series Analysis on Stock Prices & Time series forecasting of sales data
Module 5
Deep Learning Using Tensorflow:
  • Introduction to Deep Learning And TensorFlow
  • TensorFlow Classification Examples
  • Understanding Neural Networks with TensorFlow
  • Convolutional Neural Network (CNN)
  • Project on Building a CNN for Image Classification
  • Introducing Recurrent Neural Networks skflow: RNNs in skflow
  • Understanding of TFLearn APIs
  • Understanding Keras API for implementing Neural Networks
  • Real-Time Project on SPAM Prediction using RNN & Image Classifier using PyTorch
Natural Language Processing (NLP):
  • Natural Language Processing
  • Text Analysis
  • KNN
  • Use cases on NLP: Sentiment analysis for marketing
  • Text Pre-Processing Techniques
  • Stemming
  • Projects And Case Study on Sentiment analysis for Twitter, web articles & Advanced Text Analytics & NLP
Module 3 Computer Vision:
  • Computer Vision overview
  • Image Filtering
  • Image Processing
  • Image Classification and segmentation
  • Project: The Problem of Scale and Shape
  • Real-Time Use Case: Single Shot MultiBox Detector & Object Localization
Reinforcement Learning:
  • What is Reinforcement Learning - Basics
  • Approximation Methods for Reinforcement Learning
  • Projects and Case Studies on Solving Taxi Environment & Solving Frozen Lake
Model Training & Deployment Using (AWS GCP):
  • AWS (Amazon Web Services)
  • GCP (Google Cloud Platform)
  • Introduction to AWS and GCP Cloud ML Engine
  • Deploying Machine Learning Model
  • Training Machine Learning Mode

Career Opportunities

  • Machine Learning Engineer
  • Data Scientist
  • NLP Scientist
  • Software Developer/Engineer (AI/ML)
  • Human-Centred Machine Learning Designer

Entry Qualification

  • Candidates will be admitted on the basis of interviews and / or group discussions.
  • 20% of the total seats will be reserved for SC, ST and OBC candidates.If the reserved seats are not filled within the specified period, the vacant seats will be offered to the general candidates.

Course Features

Industry Experienced Trainer
4.9 (Google Review)
Study Mode
Offline & Online
2 month
English, Bengali, Hindi
100% Job Assistance
Free & Paid
Course Price
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Oxford Global Academy of Excellence Courses

MD Kashid Hossain

   I am MD Kasid Hossain. I am a student of Oxford Global Academy of Excellence, Kolkata. Here I am doing Spoken English class. Oxford Global Academy of Excellence is a very advantage platform by spoken English, computer course and more. There sirs, madams are very Helpful. All time they Support and guide us. I always enjoy my classes.