Mega-Net AcademyMega-Net Academy is a first class technology training institute with focus on Certification Training in Machine Learning with Python. This course dives into machine learning using an approachable, and well-known programming language, Python.
Get Certified in Machine Learning with Python
There's no better time to train in the exciting field of machine learning. If you’re looking for a course that covers everything from the fundamentals to advanced techniques, look no further than Mega-Net Academy comprehensive Machine Learning with Python Certification training.
Mega-Net Academy provides an array of machine learning projects for beginners, including more than 20 machine learning exercises. The course also includes 56 hours of instructor-led training and mentoring sessions from machine learning experts. Get certified today to take your career to the next level!
- Course Content
- Introduction to Python You will learn the basics of the Python Programming Language, Data Types, Lists, Tuple, Dictionary, Sets, Get introduced to Python Libraries such as Pandas, Numpy, Matplotlib, SciPy, etc for implementing machine learning algorithms.
- Introduction to Machine Learning You will learn to define and also learn about the different types of Machine Learning, supervised and unsupervised machine learning, Scikit-Learn Framework, Keras Deep Learning Framework for implementing machine learning models
- Classification You will learn about Classification technique as a type of Supervised Machine Learning. You will practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM with different datasets.
- Regression You will learn about Regression technique as a type of Supervised Machine Learning. You will learn about the different types Regression models such as Linear Regression, Ridge Regression, Lasso Regression, Polynomial Regression, Bayesian Linear Regression and practice with different datasets.
- Clustering You will learn Clustering is an unsupervised problem of finding natural groups in the feature space of input data, that there are many different clustering algorithms and no single best method for all datasets, you will also learn how to implement, fit, and use top clustering algorithms in Python with the scikit-learn machine learning library.
- Time Series Forecasting You will learn Standard definitions of time series, time series analysis, and time series forecasting, The important components to consider in time series data, Examples of time series to make your
- Forecasting Electricity Usage You will learn how to develop a framework for evaluating linear, nonlinear, and ensemble machine, learning algorithms for multi-step time series forecasting. Learn how to evaluate machine learning algorithms using a recursive multi-step time series forecasting strategy. You will learn how to evaluate machine learning algorithms using a direct per-day and per-lead time multi-step time series forecasting strategy. This is a mini project that will be done in class.
- Final Project You will do a project based of what you have learned so far. You will submit a report of your project for peer evaluation.