Machine Learning using Python
The Machine Learning using Python course offers a comprehensive, hands-on learning experience with over 40 hours of applied learning and 4 real-world projects. It covers key machine learning concepts like supervised and unsupervised learning, regression, classification, and time series modeling. With dedicated mentoring and lifetime access to materials, participants will gain the skills needed to become successful machine learning engineers or data scientists. The course also includes industry-based projects, interactive labs, and focuses on using Python and its powerful libraries like TensorFlow and scikit-learn for data-driven solutions.
About this Course
Unlock the full potential of data with our comprehensive Machine Learning using Python course. Learn from 40+ hours of applied learning, interactive labs, and real-world projects. Gain in-demand skills in supervised/unsupervised learning, regression, classification, time series modeling, and more using Python. This course is ideal for professionals seeking to upskill and transition into roles like machine learning engineer or data scientist. With dedicated mentorship, lifetime access to learning materials, and a focus on experiential learning, you’ll be well-prepared for a successful career in machine learning.
Outline
Lesson 01: Course Introduction
- Overview and learning objectives
- Introduction to Python packages for machine learning
Lesson 02: Introduction to Machine Learning
- Types of Machine Learning: Supervised, Unsupervised
- Machine Learning pipeline, MLOps
Lesson 03: Supervised Learning
- Supervised Learning algorithms
- Data preparation, detecting overfitting/underfitting
Lesson 04: Regression and Applications
- Linear, Logistic, Polynomial, Ridge, LASSO regression
- Model building and performance evaluation
Lesson 05: Classification and Applications
- Classification algorithms: Naive Bayes, KNN, Decision Trees
- Random Forest, Support Vector Machines (SVM)
Lesson 06: Unsupervised Algorithms
- Clustering techniques: K-means, Hierarchical Clustering
- Outlier detection, Principal Component Analysis (PCA)
Lesson 07: Ensemble Learning
- Bagging, Boosting, Stacking methods
- Hands-on with TensorFlow and Keras
Lesson 08: Recommender System
- Collaborative Filtering: User-based, Item-based
- PyTorch for building recommendation engines
Exam Pass Guarantee
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Raves & Praise
D. Easter
PMP Certification Training, November 2008
Thanks Microtrain for conducting a great PMP course that set me on a successful path to obtain my PMP certification. I have passed the test!
Karine Bucci
Project Management
Great class, loved the interaction and team-building exercises. Plenty of materials and learning tools supplied. Although it was a full week's class, never boring. Donna [Russell] was awesome and gave many supporting examples for better understanding.
Kaycee Ekufu
MCSA
The instructor [Al Khalfan] was very knowledgeable in the field. He presented the materials and concepts with a professional touch. He also frequently adds humor to his teachings, which made it easier for me to understand. The support staff was all nice, gentle, caring, and very helpful. The materials, rooms and amenities were excellent and exceeded expected standards.
Christopher Fowler
MCSA
Very thorough, good mix of lecture and lab work. Al is great! Keeps classes interesting and supplements required test material with need-to-know items from real world experience.
Ron Cwik
PMP Certification Training, 2008
Great training facility. Great instructors. Great experience.
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