Data Science and Machine Learning with Python
February 21, 2021 20210222 9:08Data Science and Machine Learning with Python
Data Science and Machine Learning with Python
Course Description
Despite the fact that data science is a fastgrowing 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 recallfactor 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 indepth 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 preprocessing in Python
 Train Naïve Bayes model to classify spam emails
 Test and Evaluate a classification model
 Use pretrained Deep learning models for image classification
 Build you own neural network with Keras
 Use Tensorflow to classify handwritten digits
What is the target audience?

Data Science Course Introduction
Data Science Course Introduction

Python Fundamentals
Quick Python Crash Course to get you started

Python for Data Analysis  NumPY
Using NumPY for Data Analysis

Python for Data Analysis  Pandas
Python for Data Analysis using Pandas

Python for Data Visualization
Python for Data Visualization sinf Matplotlib and Seaborn

Introduction to Machine Learning
Introduction to Machine Learning

Linear Regression
Linear Regression, Cross Validation and Bias Variance

Logistic Regression
Logistic Regression

K Nearest Neighbours
K Nearest Neighbours (KNN)

Decision Trees and Random Forests
Decision Trees and Random Forests

Support Vector Machines
Support Vector Machines (SVM)

K Means Clustering
K Means Clustering

Principal Component Analysis
Principal Component Analysis (PCA)

Natural Language Processing
Natural Language Processing (NLP)

Neural Nets and Deep Learning
Neural Nets and Deep Learning

Big Data and Spark with Python
Big Data and Spark with Python