Machine learning and Deep learning have revolutionized various industries by enabling the development of intelligent systems capable of making informed decisions and predictions. These technologies have been applied to a wide range of real-world projects, transforming the way businesses operate and improving outcomes across different domains.
In this training, an attempt has been made to teach the audience, after the basic familiarity with machine learning and deep learning, their application in some real problems and projects (which are mostly popular and widely used projects).
Also, all the coding and implementation of the models are done in Python, which in addition to machine learning, students’ skills in Python language will also increase and they will become more proficient in it.
In this course, students will be introduced to some machine learning and deep learning algorithms such as Logistic regression, multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, … and different models. Also, they will use artificial neural networks for modeling to do the projects.
The use of effective data sets in different fields, data preparation and pre-processing, visualization of results, use of validation metrics, different prediction methods, image processing, data analysis and statistical analysis are other parts of this course.
Machine learning and deep learning have brought about a transformative impact across a multitude of industries, ushering in the creation of intelligent systems with the ability to make well-informed decisions and accurate predictions. These innovative technologies have been harnessed across a diverse array of real-world projects, reshaping the operational landscape of businesses and driving enhanced outcomes across various domains.
Within this training course, the primary aim is to impart knowledge to the audience, assuming a foundational understanding of machine learning and deep learning concepts. The focus then shifts to their practical applications in addressing real-world challenges and undertaking projects, many of which are widely recognized and utilized within the field.
Moreover, the entirety of coding and models implementation is conducted using the Python programming language. This dual approach not only deepens the students’ grasp of machine learning but also contributes to their proficiency in the Python language itself.
The curriculum of this course encompasses the introduction of several fundamental machine learning and deep learning algorithms, including Logistic Regression, Multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, and some other algorithms among others, alongside diverse model architectures. As a pivotal component of the course, students delve into the utilization of artificial neural networks for modeling, which serves as the cornerstone for executing the various projects.
Comprehensive utilization of pertinent datasets spanning diverse domains, coupled with comprehensive data preparation and preprocessing techniques, takes precedence. The students are further equipped with the skills to visualize and interpret outcomes effectively, employ validation metrics judiciously, explore varied prediction methodologies, engage in image processing, and undertake data analysis and statistical analysis. These facets collectively constitute the multifaceted landscape covered by this course.
And at the end, more than 40 complete and practical cheat sheets in the field of data science, machine learning, deep learning and Python have been given to you.
Mafunzo ya mashine na Mafunzo ya kina yamebadilisha viwanda mbalimbali kwa kuwezesha maendeleo ya mifumo ya akili inayoweza kufanya maamuzi na utabiri wa habari. Teknolojia hizi zimetumika kwa aina mbalimbali za miradi ya ulimwengu halisi, kubadilisha njia ya biashara kufanya kazi na kuboresha matokeo katika nyanja mbalimbali. Katika mafunzo haya, jaribio limefanywa kuwafundisha wasikilizaji, baada ya ufahamu wa msingi wa kujifunza mashine na kujifunza kina, matumizi yao katika baadhi ya matatizo halisi na miradi (ambayo ni wengi maarufu na kutumika sana miradi). Pia, programu zote za programu na utekelezaji wa mifano hufanywa katika Python, ambayo pamoja na kujifunza mashine, ujuzi wa wanafunzi katika lugha ya Python pia utaongezeka na watakuwa na ujuzi zaidi. Katika kozi hii, wanafunzi wataanzishwa kwa baadhi ya kujifunza mashine na kujifunza kina algorithms kama vile Logistic regression, Multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, na mifano tofauti. Pia, watatumia mitandao ya neva bandia kwa ajili ya modeling kufanya miradi. Matumizi ya seti za data za ufanisi katika nyanja tofauti, maandalizi ya data na usindikaji wa awali, uonekano wa matokeo, matumizi ya vipimo vya uthibitisho, mbinu tofauti za utabiri, usindikaji wa picha, uchambuzi wa data na uchambuzi wa takwimu ni sehemu nyingine za kozi hii. Mafunzo ya mashine na kujifunza kina kumeleta athari ya mabadiliko katika tasnia nyingi, ikisababisha uundaji wa mifumo ya akili na uwezo wa kufanya maamuzi ya habari na utabiri sahihi. Teknolojia hizi za ubunifu zimetumika katika miradi mbalimbali ya ulimwengu halisi, kuunda upya mazingira ya uendeshaji wa biashara na kuendesha matokeo yaliyoimarishwa katika maeneo mbalimbali. Katika kozi hii ya mafunzo, lengo kuu ni kutoa maarifa kwa watazamaji, ikidhani ufahamu wa msingi wa kujifunza mashine na dhana za kujifunza kina. Lengo kisha hubadilika kwa matumizi yao ya vitendo katika kushughulikia changamoto za ulimwengu halisi na kutekeleza miradi, ambayo mengi ni kutambuliwa sana na kutumika ndani ya uwanja. Zaidi ya hayo, jumla ya coding na mifano utekelezaji ni uliofanywa kwa kutumia Python lugha ya programu. Njia hii mbili si tu kina wanafunzi 'kuelewa ya kujifunza mashine lakini pia inachangia ujuzi wao katika lugha ya Python yenyewe. Mafunzo ya kozi hii ni pamoja na kuanzishwa kwa algorithms kadhaa za msingi za kujifunza mashine na kujifunza kina, ikiwa ni pamoja na Logistic Regression, Multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, na baadhi ya algorithms nyingine, pamoja na usanifu mbalimbali wa mfano. Kama sehemu muhimu ya kozi, wanafunzi kuchunguza katika matumizi ya mitandao ya neva bandia kwa ajili ya modeling, ambayo hutumika kama jiwe la msingi kwa ajili ya kutekeleza miradi mbalimbali. Matumizi ya kina ya seti za data husika zinazoenea katika nyanja mbalimbali, pamoja na utayarishaji wa kina wa data na mbinu za kabla ya usindikaji, hupata kipaumbele. Wanafunzi pia kuwa na ujuzi wa visualize na kutafsiri matokeo kwa ufanisi, kutumia metrics uthibitisho busara, kuchunguza mbinu mbalimbali utabiri, kushiriki katika usindikaji wa picha, na kufanya data uchambuzi na uchambuzi wa takwimu. Mambo hayo yote yanafanyiza mazingira yenye pande nyingi yanayozungumziwa katika somo hili. Zaidi ya 40 cheat sheets kamili na vitendo katika uwanja wa data sayansi, kujifunza mashine, kujifunza kina na Python.