This Machine Learning training in Houston by Intellipaat is specially created to enrich the learning process of an individual, by not just focusing on the theoretical concepts but implementing them in real-time projects. The course content is prepared by highly qualified SMEs, and the instructor of this Machine Learning certification course introduces learners to different real-life use cases that are to be solved using Machine Learning techniques. This will boost the learning process and also the implementation skills of the learners. Therefore, you should definitely go for the best Machine Learning course in Houston offered by Intellipaat.
People who are interested in Machine Learning and want to make a bright career in it should enroll in this course provided by one of the most esteemed Machine Learning Institutes in Houston.
According to Glassdoor, the average salary of a certified Machine Learning Engineer in India is ₹945,000 and, in the United States, it is US$128,500. Also, the number of job posting for a Machine Learning Engineer in January 2020 was more than 8,000. This indicates the huge demand for Machine Learning Engineers in the market currently.
To master Machine Learning, you need to nourish your problem-solving and implementation skills. This helps in enhancing conceptual knowledge. In this Machine Learning course in Houston, you will work on various projects based on e-commerce, insurance, healthcare, etc. By working on different real-life use cases, you will intensify your problem-solving and implementation skills. By having a Machine Learning certification in Houston, it increases the possibilities of getting employed.
Machine Learning has a great future as it is widely used for automation in various sectors. Various researches are going on to improve Machine Learning techniques to foster automation. Due to this, the job market for a Machine Learning Engineer is hiking its peak. By taking up this one of the finest Machine Learning courses in Houston, you will get a certificate that you can add to your resume to build a strong ML Engineer profile and make a lucrative career.
1.1 Need of Machine Learning
1.2 Introduction to Machine Learning
1.3 Types of Machine Learning, such as supervised, unsupervised and reinforcement learning, why Machine Learning with Python and applications of Machine Learning.
2.1 Introduction to supervised learning, types of supervised learning, such as regression and classification
2.2 Introduction to regression,
2.3 simple linear regression,
2.4 Multiple linear regression, assumptions in linear regression
2.5 Math behind linear regression.
Hands-on Exercise
1. Implementing linear regression from scratch with Python
2. Using Python library Scikit-learn to perform simple linear regression and multiple linear regression
3. Implementing train–test split and predicting the values on the test set.
3.1 Introduction to classification
3.2 Linear regression vs Logistic regression
3.3 Math behind logistic regression, detailed formulas, log it function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR.
Hands-on Exercise
1. Implementing logistic regression from scratch with Python
2. Using Python library Scikit-learn to perform simple logistic regression and multiple logistic regression
3. Building a confusion matrix to find out accuracy, true positive rate, and false positive rate.
4.1 Introduction to tree-based classification
4.2 Understanding a decision tree, impurity function, entropy, to understand the concept of information gain for the right split of node, impurity function, information gain
4.3 Understand the concept of information gain for the right split of node, impurity function, Gini index, Understand the concept of Gini index for the right split of node. Understand Overfitting, pruning, Pre-Pruning, Post-Pruning and cost-complexity pruning
4.4 Introduction to ensemble techniques, understanding bagging, introduction to random forests, and finding the right number of trees in a random forest.
Hands-on Exercise
1. Implementing a decision tree from scratch in Python
2. Using Python library Scikit-learn to build a decision tree and a random forest.
3. Visualizing the tree and changing the hyper parameters in the random forest.
5.1 Introduction to probabilistic classifiers,
5.2 Understanding Naïve Bayes, math behind the Bayes theorem
5.3 Understanding a support vector machine (SVM)
5.4 Kernel functions in SVM, and math behind SVM.
Hands-on Exercise
1. Using Python library Scikit-learn to build a Naïve Bayes classifier and a support vector classifier.
6.1 Types of unsupervised learning, such as clustering and dimensionality reduction, types of clustering
6.2 Introduction to k-means clustering
6.3 Math behind k-means
6.4 Dimensionality reduction with PCA.
Hands-on Exercise
1. Using Python library Scikit-learn to implement K-means clustering
2. Implementing PCA (principal component analysis) on top of a dataset.
7.1 Introduction to Natural Language Processing (NLP)
7.2 Introduction to text mining
7.3 Importance and applications of text mining
7.4 How NPL works with text mining
7.5 Writing and reading to word files
7.6 OS modules, Natural Language Toolkit (NLTK) environment,
7.7 Text mining: its cleaning and pre-processing and text classification.
Hands-on Exercise
1. Learning Natural Language Toolkit and NLTK Corpora
2. Reading and writing .txt files from/to a local drive
3. Reading and writing .docx files from/to a local drive.
8.1 Introduction to Deep Learning with neural networks
8.2 Biological neural network vs artificial neural network
8.3 Understanding perception learning algorithm, introduction to Deep Learning frameworks, and Tensor Flow constants, variables and place-holders.
9.1 What is time series?, its techniques and applications
9.2 Time series components
9.3 Moving average, smoothing techniques, exponential smoothing
9.4 Univariate time series models
9.5 Multivariate time series analysis
9.6 ARIMA model, time series in Python
9.7 Sentiment analysis in Python (Twitter sentiment analysis), and text analysis.
Hands-on Exercise
1. Analyzing time series data
2. The sequence of measurements that follow a non-random order to recognize the nature of phenomenon
3. Forecasting the future values in the series.
Project 01: Analyzing the trends of COVID-19 with Python
Industry: Analytics
Problem Statement: Understanding, the trend of COVID 19 spread and if the restrictions imposed by governments around the world has helped us curb the COVID 19 cases and by what degree
Topics: In this project we will use Data Science and Python to perform data visualization to understand the data. We will use on COVID 19 data set to do Time Series Analysis in order to make a prediction about future cases if the current trend as observed thus far continues.
Highlights:
Project 02 – Customer Churn Classification
Topics – This is a real-world project that gives you hands-on experience in working with most of the Machine Learning algorithms.
The main components of the project include the following:
Project 03 – Recommendation for Movie, Summary
Topics – This is a real-world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project requires you to deeply understand information filtering, recommender systems, user ‘preference’, and more. You will exclusively work on data related to user details, movie details, and others.
The main components of the project include the following:
Case Study 01 – Decision Tree
Topics – To understand the structure of a dataset (PIMA Indians Diabetes database) and create a decision tree model based on it by using Scikit-learn
Case Study 02 – Insurance Cost Prediction (Linear Regression)
Topics – To understand the structure of a medical insurance dataset, implement both simple and multiple linear regression, and predict values
Case Study 03 – Diabetes Classification (Logistic Regression)
Topics – To understand the structure of a dataset (PIMA Indians Diabetes dataset), and implement multiple logistic regression and classify[I3] . Fit your model on the test and train data for prediction and evaluate your model using confusion matrix and then visualize it
Case Study 04 – Random Forest
Topics – To create a model that would help in classifying whether a patient is ‘Normal’, ‘Suspected to have disease,’ or in actuality ‘Has the disease’ on the ‘Cardiotocography’ dataset
Case Study 05 – Principal Component Analysis (PCA)
Topics – Read the sample iris dataset given to you, use PCA to figure out the number of most important principal features, and then reduce the number of features using PCA. Train and test the Random Forest Classifier algorithm to check if reducing the number of dimensions is causing the model to perform poorly. Figure out the most optimal number that produces good quality results and predicts accuracy
Case Study 06 – K-means Clustering
Topics
Intellipaat Machine Learning certification is well recognized across more than 500+ top MNC’s. As part of this ML training, you will be engaged in real-time projects and assignments which projects real-world industry scenarios. This way, you can expedite your career effortlessly.
Intellipaat Course Completion Certification will be issued after the project has been completed (after expert review) and upon scoring at least 60 percent on the quiz.
You would be glad to know that Intellipaat certification is recognized by more than 100 top multinational companies, including Cisco, Ericsson, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered Bank, IBM, Infosys, Genpact, TCS, Hexaware and more.
Our Alumni works at top 3000+ companies
They provide one of the best Machine Learning online courses in the market. I really liked the way Intellipaat approached such a complex topic like Machine Learning and made it so easy to understand. 5 stars to Intellipaat for conducting such wonderful Machine Learning classes.
Intellipaat's Machine Learning online training helped me clear the exam in the first attempt. Also, the trainer came with very good experience, and thus, I was able to learn this technology effortlessly. Thank you Intellipaat!
I had completed this course a while back and the job assistance they provided me with was extremely supportive. Their team offered mind-blowing support in terms of upgrading my resume with respect to the new skills I had acquired during this course. They also conducted several mock interviews which gave me confidence during my actual interview and helped me successfully land the job. Overall, it is the best online course for machine learning.
This ML online course was up-to-date and the instructor taught all the concepts in great detail. It was a very informative course with all the topics taught step-by-step. For a beginner like me, this Deep Learning course was amazing and helped me a lot in becoming proficient in Machine Learning and Deep Learning concepts.
Overwhelming Machine Learning training course with concepts taught in great detail. The 24-hours support provided was really helpful when I had to clear my doubts. Moreover, the resource material provided in this course was comprehensive and interesting, which helped in making this course fun to learn.
I have done a couple of online courses in various courses from other places but I was never quite satisfied. I recently took up one of the best Machine Learning courses with Intellipaat and I am still in the middle it. But, unlike my previous experiences, this course was very interactive and really helped me grasp all the concepts with a decent amount of effort.
I have always had a keen interest in Machine Learning but I was not sure about doing an online course in it since I do not really have much experience in learning online. However, I came across one of these machine learning classes and I thought of giving it a try, and to say that I was extremely satisfied with this course would be an understatement.
I am a Machine Learning Engineer in a well-reputed organization. I started working here recently and I got this job opportunity only because of this best course for Machine Learning. I finished this course recently and the job opportunities that came flooding in after I had received the certification was amazing.
Overall, the most delightful aspect of the best Machine Learning course online was the assistance of the placement team. The team really did a great job in preparing me for a job interview by conducting a number of dummy interviews for which I am extremely grateful. They also help me prepare and mold my resume as per the job role I was seeking.
The concepts are taught from the basics which were really helpful for me as I am not from a computer science background. Moreover, the course material was easily available and accessible even after I had completed the course. This was convenient and helpful since I had to refer to a few concepts every now and then to completely grasp it.
Excellent Machine Learning certification course with extremely interactive sessions. The course was very well delivered and structured. Even the complex concepts were taught with extreme ease with practical, real-life, and relatable examples. This helped a great deal in getting hold of the concepts and comprehending the same.
The training was amazing and the trainer did a great job of explaining it. He was extremely patient throughout the lectures and gave enough time and effort to explain each and every concept. I was extremely satisfied with this Machine Learning python course.
I had recently completed my AI and Machine Learning course with Intellipaat. All the concepts were clearly explained by the trainer. I was able to understand and grasp the concepts easily. Also, I could clear all my doubts related to the course on the given platform.
Intellipaat provides comprehensive Machine Learning training through hands-on projects and case studies. A few of the many reasons for choosing Intellipaat ML course includes:
