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
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Ron Cwik
PMP Certification Training, 2008
Great training facility. Great instructors. Great experience.
Scott Wilson - Team Bradley
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Dawn has been a pleasure to work with. She is diligent in her efforts to give MicroTrain’s students the best opportunities to connect and network with various companies and players in the job market. The curriculums and certification programs are in line with today’s current IT needs. It’s good to see that they evolve with what employers are most seeking out there. I would recommend to any employer that hires skilled IT workers that they look into MicroTrain’s career services department as a potential source of candidates. The business of hiring can be quite costly, and Dawn and her staff provide a free service to outside companies. This value-add for MicroTrain’s students aligns with their mission in both training and job assistance to set up a win/win scenario for all involved.
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.
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Robert Graf
MCSA
Microtrain presents the course material in an interesting and concise manner. Microtrain gives the student all the resources necessary to pass the certification exams. Both the Chicago and Lombard facilities were excellent, and the staff goes to any length to accommodate any student changes.
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