Data Science and Machine Learning with Python

  • Categories DATA ANALYSIS
  • Total Enrolled 0
  • Last Update August 21, 2020


Despite the fact that data science is a fast-growing field with limitless potentials, it can be a daunting career to explore without proper expert guidance.
At Loctech we have expert data scientists with years of industry experience to guide you through every step of the way in your journey to becoming a Data Scientist.

Towards the end of this course you will be exposed to building predictive models in Python. You will understand machine learning and clearly distinguish between various types of machine learning (supervised & unsupervised machine learning) and the various scenarios each can be utilized. At this time, you will be able to test the performance of your machine learning models as well as how to optimize the precision, accuracy and recall-factor of your model. You will equally learn the various types and categories of machine learning algorithms and the types of problems they are suitable for.

At this point in the course you will learn deeper machine learning techniques such as underfitting/overfitting, regularization, hyperparameter optimization, cross validation, normalization and standardization. Most importantly you will learn how to save your trained models through model persistence using Pickle and how to build machine learning API endpoints to make your solution available over a network or the internet.

Finally, at the completion of this course you would have an in-depth knowledge in the field of data science and can apply the knowledge to solve any problem provided that data is available or pursue more advanced knowledge in the field of data science.



Main Features

  • Introduction to Python Programming
  • Data Science Workflow and Python Optimization
  • Introduction to linear regression
  • Multivariable Linear Regression using Matplotlib and Seaborn
  • Transform and improve data model using Baysian Information Criterion
  • Classification problems, probability and data pre-processing in Python
  • Train Naïve Bayes model to classify spam emails
  • Test and Evaluate a classification model
  • Use pre-trained Deep learning models for image classification
  • Build you own neural network with Keras
  • Use Tensorflow to classify handwritten digits

What is the target audience?

  • A little or less knowledge on Python programming would be an added advantage
  • A good knowledge of computer appreciation


Who this Data Science and Machine Learning course is for
  • Data Analyst
  • Business Intelligence Officers
  • Machine Learning Enthusiast
  • Programmers