Machine Learning with Python

Overview

Machine Learning (ML) is an application of Artificial Intelligence(AI) that provides systems the ability to automatically learn and improve from experience without being explicit. This course is designed to equip you with knowledge, skills, and implementation experience of Machine Learning.

Course Level: Beginner to Intermediate

Course Type: Online Live (Google Meet)

Who Should Enroll?

  • This course is intended for anyone who has knowledge of any type of programming language.

Free Webinar on Machine Learning

  • Date: 25 September 2021
  • Time: 09:00 PM - 10:00 PM

Registration Form

N.B. Please do not use any symbol in the Name field.

Registration Form

N.B. Please do not use any symbol in the Name field.

Aminul Islam

AI/ML Engineer

“Driven AI/ML Software Engineer along with a Data Scientist’s mindset” always eager to thrive in demanding Artificial Intelligence-based Software Development. Well-informed on the latest Machine Learning advancements. Ready to combine state-of-the-art techniques with a desire to exploit cutting-edge AI and Data Science technology. Experienced in Machine Learning and Deep Learning-based development using Python, TensorFlow-Keras or PyTorch, Scikit-Learn, and so on. Worked in Computer Vision and Natural Language Processing related end-to-end software development and R&D.

Nayeem Ahmad

Managing Director & CEO, Ishraak Solutions Limited

B.Sc. and M.Sc. from CSE, BUET, and MMTP from IIT Kharagpur, India. Over 18 years of experience in the software industry. He is a prominent Software Professional, a Tech Enthusiast, and an entrepreneur. Working as the CEO of Ishraak Solutions Limited. Previously he was the CEO of Green IT Solutions, COO at CTrends, and an Agile Evangelist at Infolytx. He also worked at ValuePlus as a Director and CTO. He has got CSP, CSPO, CSM certifications from ScrumAlliance. As an entrepreneur, he founded Lemono Apps. He also was the IT manager and the lecturer at United International University. He has a keen interest in Digital Transformation, Artificial Intelligence/Machine Learning/Deep Learning, Blockchain, etc.

Upcoming Batches

Starting From
Coming Soon
48 Hours30 hrs (Module 1) + 18 hrs (Module 2)
8 Classes5 Classes (Module 1) + 3 Classes (Module 2)
Saturday (10 AM – 6 PM)(Lunch Break 2 Hours)
Instructor
Starting From
Coming Soon
48 Hours30 hrs (Module 1) + 18 hrs (Module 2)
8 Classes5 Classes (Module 1) + 3 Classes (Module 2)
Saturday (10 AM – 6 PM)(Lunch Break 2 Hours)
Instructor
Starting From
Coming Soon
48 Hours30 hrs (Module 1) + 18 hrs (Module 2)
16 Days10 days (Module 1) + 6 days (Module 2)
Saturday (10 AM – 3 PM)(Lunch Break 1 Hour)
Thursday (7 PM – 9 PM)(Hands-on & Problem Solving)
Instructor

Course Outline

Module - 1

  • Introduction to Machine Learning (ML)
  • Environmental Setup
  • Introduction to ML libraries for Python (Scikit learn, PyTorch, tensor, Keras)
  • Data preprocessing and introduction to pandas, NumPy, and matplotlib
  • Supervised Learning
    • Regression
      • Single Linear Regression
      • Multiple Linear Regression
      • Polynomial Regression
      • Support Vector Regression
      • Decision Tree Regression
      • Random Forest Regression
  • Classification
    • Logistic Regression
    • KNN Classification
    • SVM Classification
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification
  • Project 1 (Boston House price prediction)
  • Unsupervised Learning
    • Clustering
      • K-Means Clustering
      • Hierarchical Clustering
  • Project 2 (Mall Customer Clustering Analysis)
  • Model selection
    • L1 & L2 regularization
    • K-Fold Cross-Validation
    • Stratified K-Fold
    • Classification metrics
      • Confusion matrix
      • Accuracy
      • Precision
      • Recall
      • F1 Score
      • AUC-ROC
  • Regression metrics
    • Mean Squared Error or MSE
    • Root Mean Squared Error or RMSE
    • Mean Absolute Error or MAE
    • R-Squared
  • Dimensionality Reduction
    • PCA
    • LDA
  • Practice Session
  • Implementation of model optimization and selection

Module - 2: For University Students

  • Introduction to NLP
    • Regular expression
    • Extract Linguistic Features
    • Bag of Words
    • TF-IDF
    • Word embedding
  • Project 3 (Toxic Comment/Sentiment Analysis)
  • Introduction to Deep Learning
  • Neural Network
  • Convolutional Neural Network architecture
    • Convolutional Layer
    • Pooling Layer
    • Fully Connected Layer
    • Dropout
    • Activation Functions
  • Hands-on Class - 13 Topics
  • Project 4 (Numeric Number Detection)
  • Final project Discussion
  • Final Project Review

How Will You Benefit From the Course?

  • Increase your career prospects and employability with the latest skill-set.
  • Gain the skills to easily identify the trends and patterns in data-sets.
  • Acquire Knowledge of handling multi-dimensional and multi-variety data.
  • Learn about the wide variety of practical applications of ML.
  • Get a professional certificate certified by industry experts.
  • Each class will be recorded and the video provided will be at the end of the class.
  • There will be opportunities to share any type of programming problem in the group.
  • At the beginning of each class, a quiz test will be taken on the topic of the previous class.
  • Learn how to conduct system automation without human intervention and make continuous improvements.

Frequently Asked Questions

What programming language will be used for this training?

We will use Python for the hands-on during the class.

We will use Python. But if someone is comfortable with R, he/she can easily try the same code.

We provide Presentations, Links to online resources, PDF Books, a list of study materials, and also sample codes.

Individual projects are not covered in this course. But, the course will cover all the required topics necessary to take up any real-life projects.

Yes, with each topic we cover, relevant code is also shared and explained during the sessions.

Rules & Regulations

  • The commencement of classes is subject to receipt of a satisfactory number of participants. SkillXprss has the right to cancel/postpone the classes.
  • Training fees include venue, lunch & refreshments, stationery, certificate, etc.
  • Requests for cancellation of registration must be shared at least 7 days before the start date of the class. In that case, the participant will get a full refund of the registration fee.
  • If the class schedule/date is changed by SkillXprss, the participant may request to cancel the registration. He/She will get a full refund in that scenario.
  • SkillXprss shall not be responsible in any circumstances beyond our control that may need to postpone or cancel the program or cancellation of any other expenses incurred by the participant due to such instance.

SkillXprss is one of the leading skill assessment and career development platforms in Bangladesh. We offer the most demanding skill tests, training courses, and flexible workshops for both professionals and learners.

Other Links

Contact Us

  • House 76 (Level 1), Road 04, Block B
  • Niketan, Gulshan 1, Dhaka 1212
  • Bangladesh

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